2.1 Observational dataset

A complete description and overview of the project is provided by Redemann et al. (2020). All measurements were taken aboard the National Aeronautics and Space Administration (NASA) P-3B aircraft from 12 August through 31 August as part of the ORACLES 2017 campaign. The aircraft was based at the São Tomé International Airport (0.3778^∘ N, 6.7131^∘ E) of São Tomé, an island off the west coast of central Africa. A map of MODIS satellite fires for the month of August 2017 can be found in Fig. 1. The burning area is largely savanna grassland, and the subsequent smoke plume travels westward over the SEA region. This work focuses on data collected on eight different research flights in the 2017 campaign during which instrumentation providing all relevant aerosol-microphysical and cloud-scale-dynamics data performed optimally. Flight paths for all data used in this work can be found in Fig. 1. Most flights followed a routine route going out to 5^∘ E longitude and then due south. Each flight included legs at varying altitudes to capture the characteristics of the plume, the marine boundary layer (MBL), and the cloud deck. This work primarily focuses on the aerosol measured below cloud in the MBL, as that is the aerosol that will participate in cloud droplet activation.

Figure 1Map of ORACLES 2017 research flights used in this work, together with aerosol optical thickness (AOT) of the August 2017 plume (Meyer et al., 2015). The inset provides MODIS imagery of savanna fires throughout August 2017. Most flights are in close proximity to the routine flight path of due south along a 5^∘ E longitude.

2.2 Instrumentation

A summary of the relevant measurements obtained at each flight can be found in Table 1. A solid diffuser inlet, characterized previously as having a 4 µm dry-diameter cutoff (McNaughton, et al., 2007), was used to sample aerosol onboard the aircraft. A Droplet Measurement Technologies (DMT) CCN-100 continuous-flow streamwise thermal-gradient chamber (CFSTGC; Roberts and Nenes, 2005) was used to measure CCN concentrations using a DMT constant pressure inlet operated at 600 mbar pressure. Since CCN measurements are highly sensitive to fluctuations in pressure and their effect on generated supersaturation (Raatikainen, et al., 2014), a flow orifice and active control system were used upstream of the instrument to ensure that the pressure remained constant, despite fluctuations in ambient pressure with altitude. The instrument was operated in both standard mode, where supersaturation (SS %) was stepped between 0.1, 0.2, and 0.3 % by changing the temperature gradient in the droplet growth chamber, and in scanning-flow CCN analysis (SFCA) mode (Moore and Nenes, 2009), where supersaturation was varied from 0.1 % to 0.4 % by cycling the flow in a sinusoidal pattern from 300 to 1000 cm³ min⁻¹ while maintaining a constant temperature gradient in the growth chamber. Aerosol particles that activated into droplets sized greater than 0.5 µm were then counted as CCN by the optical particle counter located at the exit of the CFSTGC growth chamber.

Table 1Average marine-boundary-layer (MBL) aerosol concentrations from the UHSAS CCN activity derived from in situ CCN measurements (κ_CCN), bulk chemical composition (κ_AMS), and characteristic vertical updraft velocity (w^∗). Aerosol conditions are classified for each flights as polluted, intermediate, or clean based on the MBL aerosol concentration.

^a CF: Constant-flow operation of the CCN instrument. ^b SFCA: Scanning-flow CCN analysis operation of the CCN instrument. ^c Both operation modes (CF, SFCA) of the CCN instrument were used. ^d $w^{\ast }=\mathrm{0.3}$ ms⁻¹ assumed when calculating the droplet number. This value was selected based on the pollution category, date, and the average of corresponding w^∗ determined from RF10 and RF11.

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A DMT ultra-high-sensitivity aerosol spectrometer (UHSAS) was also operated on the same 600 mbar constant pressure inlet as the CFSTGC to detect the aerosol concentration from 80 to 1000 nm (Table 1). Comparison of UHSAS with DMA distributions revealed that the UHSAS counting efficiency dropped below about 80 nm (Stephen Howell, personal communication, 2020), which should not strongly affect our subsequent analysis – as particles larger than 80 nm in diameter contribute the exclusive majority of CCN that activate into droplets for the conditions considered. The aerosol size distribution was combined with CCN measurements to calculate the hygroscopicity parameter, κ, of the observed aerosol (Petters and Kreidenweis, 2007), following a procedure adopted in numerous studies (e.g., Kalkavouras et al., 2019; Bougiatioti et al., 2016; Moore et al., 2011; Lathem et al., 2013), where integration of the particle size distribution from the largest resolved bin in the UHSAS down to a characteristic size, d_crit (also known as the critical diameter), matches the measured CCN concentration. The hygroscopicity then is obtained from d_crit and the instrument supersaturation, following Kalkavouras et al. (2019).

Vertical winds on the P-3B were measured with the NASA Turbulent Air Motion Measurement System (TAMMS; Thornhill et al., 2003). Fast-response flow-angle, pressure, and temperature sensors combined with a GPS-corrected inertial navigation system (INS) provide 50 Hz inputs to compute 20 Hz averaged vertical winds via the full air motion equations from Lenschow (1986). The updraft velocities are then used as an input to calculate cloud droplet number concentration via a Gaussian distribution of updraft velocities (Sect. 2.3).

An Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS; DeCarlo, et al., 2006) was used to monitor the bulk chemical composition of sampled aerosol throughout all flights. The bulk chemical composition acquired is then used to calculate the bulk κ (Petters and Kreidenweis, 2007), based on the mass fraction of organics and sulfate in the aerosol – assuming that the hygroscopicity of the organic fraction, κ_org, equals 0.1, and of sulfate, κ_sulfate, equals 0.6. We have also ignored the effects of insoluble material – such as black carbon – as it constitutes a small volume fraction of the aerosol and has a negligible influence on hygroscopicity. The bulk-derived κ allows for comparison with the directly calculated κ from the CFSTGC and UHSAS measurements, even if the AMS-derived values correspond to larger sizes than the CCN-derived κ. Nevertheless, strong agreement is found between the two κ values (Table 1; Fig. S1 in the Supplement), thus confirming that the internal-mixture assumption inherent to CCN-derived hygroscopicity applies and that the composition varies little over the size range between d_crit (∼100–200 nm) and the peak of the mass distribution resolved by the AMS. It should also be noted that all of the AMS data analysis was in high-sensitivity mode; the AMS heater was operated at an indicated 600 ^∘C, which was tested and proved optimal for the ORACLES BB organic-aerosol plume. The data were processed using the standard AMS software (Squirrel, version 1.41).

A DMT cloud droplet probe (CDP) was used to measure the number of cloud droplets from 2 to 50 µm in diameter. The CDP was modified according to Lance et al. (2010) to reduce coincidence problems. The total cloud droplet number (N_d) from the CDP is compared against the predicted N_d from the cloud droplet parameterization. These comparisons are done in flights with mostly stacked legs in the MBL and clouds; occasionally, flights where aerosol and cloud were immediately before or after each other were used (but not stacked).

2.3 Predicted cloud droplet number

The droplet activation process is the direct microphysical link between clouds and aerosol. Every aerosol particle, to activate and form a cloud droplet, requires exposure to a critical supersaturation level (or above) for enough time to grow past a critical wet size (Nenes et al., 2001) that ensures unconstrained growth. Applying this principle to ambient clouds is confounded by the complex relationship of supersaturation with aerosol size distribution, hygroscopicity, and the characteristic vertical updraft velocity. State-of-the-art cloud droplet parameterizations (e.g., Ghan et al., 2011; Morales Betancourt and Nenes, 2014), however, resolve this relation and determine the cloud droplet number (N_d); maximum available supersaturation (S_max); and sensitivity of N_d to changes in aerosol concentration (N_a), vertical updraft velocity (w), and CCN activity (κ).

In this study, we utilize the Nenes and Seinfeld (2003) parameterization with improvements introduced by Fountoukis and Nenes (2005), Barahona et al. (2010), and Morales and Nenes (2014). In applying the droplet parameterization, we integrate over the distribution of vertical velocities within the boundary layer – by utilizing the characteristic-vertical-velocity approach of Morales and Nenes (2010). In this approach, instead of numerically integrating over a probability density distribution (PDF), the parameterization is applied at a characteristic velocity, w^∗, that yields the same result as the integrated value over the PDF. To derive w^∗, the measured updrafts (positive vertical winds), w, are taken from all segments just below cloud in a given flight then fit to a Gaussian distribution with zero mean; $w^{\ast }=\mathrm{0.79}{\mathit{\sigma }}_{\mathrm{w}}$, where σ_w is the width of the vertical-velocity spectrum (Morales and Nenes, 2010). Stratocumulus clouds, such as those sampled in this study, are well characterized by a Gaussian distribution of vertical velocities with a mean close to zero (Morales and Nenes, 2010). A comparison between the predicted N_d from the parameterization and the measured N_d from the CDP can be found in Fig. S2. The parameterized N_d was, on average, within 20 % of the measured N_d, which is within the difference range of previous droplet closure studies (e.g., Meskhidze et al., 2005; Fountoukis et al., 2007; Morales et al., 2011).

3.1 Marine-boundary-layer air mass characterization

Characteristic vertical profiles of CCN concentrations from 0.1 to 0.4 % supersaturation for flights used in this work are shown in Fig. 2. Earlier flights (RF01–RF03) have lower BB plume heights, relatively little vertical variation in concentration within the plume, and high CCN concentrations in the marine boundary layer (MBL). Later flights (RF08–RF12) show distinct layering in the plume, a higher plume cap altitude, and lower MBL concentrations. Hereafter we focus on aerosol concentrations in the MBL, being the relevant aerosol providing CCN for BL cloud formation. A summary of the MBL aerosol concentrations, CCN-derived κ (averaged over all the supersaturations measured), and characteristic vertical updraft velocity (w^∗) is provided for all flights in Table 1. Flights are classified according to the observed MBL aerosol concentrations from the UHSAS into categories defined, for the purposes of this work, as polluted (exceeding 800 cm⁻³), intermediate (500–800 cm⁻³), and clean (below 500 cm⁻³). MBL aerosol concentration is higher earlier on in August and decreases as the mission progresses. The average CCN-derived κ for the MBL aerosol is fairly consistent, ranging from 0.2 to 0.4, and agrees well with the κ estimated from the bulk MBL aerosol elemental composition as measured by the aerosol mass spectrometer, implying that the aerosol is chemically uniform throughout the ultrafine-aerosol size range (Fig. S1).

Figure 2Vertical profiles (altitude in meters) of CCN concentration (cm⁻³) from 0.1 to 0.4 % supersaturation for all flights in this work.

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Characteristic vertical updrafts are higher earlier in August, averaging 0.4 ms⁻¹, and decrease to around 0.3 ms⁻¹ later in the campaign. A decrease in MBL aerosol concentration is also seen during this time, with earlier flights seeing aerosol concentrations reaching up to 1000 cm⁻³ and later decreasing to 200 cm⁻³. The average BB plume aerosol concentrations aloft range from around 1250 to 3000 cm⁻³ but show no distinct trends throughout the month. However, an interesting trend can be found in comparing the altitudes of the bottom of the BB plume and the top of the MBL cloud deck with the characteristic vertical updraft velocities: a lower w^∗ of 0.3 ms⁻¹ coincides with an observation of a clean, low-aerosol gap between the top of the MBL clouds and the bottom of the BB plume. In higher w^∗ flights (0.4 ms⁻¹), the BB plume extends all the way down to the top of the MBL cloud layers. In these flights, the BB plume is observed to have one single well-mixed layer throughout, while the later flights ($w^{\ast }\sim \mathrm{0.3}$ ms⁻¹) are characterized by two distinct layers in the plume.

3.2 Predicted droplet number and maximum supersaturation

Figure 3a presents the predicted droplet number (N_d) and CCN (at 0.1 % supersaturation) as a function of total aerosol concentration (N_a) for the marine-boundary-layer (MBL) legs of all flights. Above an aerosol concentration of ∼600 cm⁻³, droplet number concentration becomes progressively less responsive to further increases in CCN number (as the incremental change in N_d is less as CCN increases) and becomes effectively insensitive ($\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}\sim \mathrm{0}$) for an aerosol concentration exceeding ∼1000 cm⁻³. The reason behind this increasing insensitivity can be seen in Fig. 3b, which presents N_d against N_a for all the MBL leg data; the data are colored by supersaturation. For low values of N_a and N_d (∼200 cm⁻³), S_max tends to be high (just over 0.2 %) and the response of N_d to increases in aerosol is strong. However, when transitioning from clean to intermediate MBL conditions, N_d is less sensitive to increases in aerosol because S_max decreases and mitigates some of the expected droplet number response. Upon reaching polluted conditions (>800 cm⁻³), the decrease in S_max is even stronger, entering into a regime where any additional aerosol can no longer substantially augment cloud droplets, owing to the extreme competition of the high CCN concentrations for water vapor. This water-vapor-limited regime occurs when the S_max is less than 0.1 % (Fig. 3b); given that water vapor availability is generated through expansion cooling in updrafts, this type of limitation is also known as the updraft-limited regime of droplet formation (Ruetter et al., 2009).

Figure 3(a) The predicted droplet number (N_d; cm⁻³) and measured CCN (cm⁻³) at 0.1 % supersaturation as functions of marine-boundary-layer aerosol concentration (N_a; cm⁻³) for all flights. (b) N_d against N_a in the MBL for all flights, colored by maximum in-cloud supersaturation (S_max).

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3.3 Droplet number sensitivity

The previous section pointed out the variable sensitivity of the droplet number to aerosol perturbations, depending on the conditions of cloud formation. To further explore such issues, we explicitly calculate the sensitivities (partial derivatives) of the droplet number in the MBL to changes in the aerosol number, characteristic vertical updraft velocity, and CCN activity, computed by the parameterization using a finite-difference approximation. This is shown in Fig. 4 for $\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}$ (Fig. 4a), $\partial N_{\mathrm{d}}/\partial w$ (Fig. 4b), and $\partial N_{\mathrm{d}}/\partial \mathit{\kappa }$ (Fig. 4c). Results are shown for three flights, corresponding to each pollution class of Table 1: polluted (RF02), intermediate (RF09), and clean (RF10). Sensitivity of the droplet number to total aerosol concentration ($\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}$) is fairly comparable between the two lower-concentration conditions and approaches insensitivity ($\partial N_{\mathrm{d}}/\partial d{N}_{\mathrm{a}}<\mathrm{0.1}$) when the total aerosol concentration exceeds 1000 cm⁻³. Maximum in-cloud supersaturation decreases steadily as N_a increases, and $\partial N_{\mathrm{d}}/\partial d{N}_{\mathrm{a}}$ appreciably decreases when S_max drops below 0.12 % (Fig. 4a).

Figure 4The sensitivity of the droplet number to (a) aerosol number ($\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}$), (b) characteristic velocity ($\partial N_{\mathrm{d}}/\partial w^{\ast }$), and (c) hygroscopicity parameter ($\partial N_{\mathrm{d}}/\partial \mathit{\kappa }$) as functions of N_a (cm⁻³). The data are clustered using the polluted, intermediate, and clean groupings of Table 1.

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As $\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}$ decreases with increasing levels of aerosol, droplet sensitivity to vertical updraft velocity, $\partial N_{\mathrm{d}}/\partial w$, becomes increasingly important and completely dominates droplet variability for high aerosol numbers. The reason why droplets become so sensitive to vertical-velocity fluctuations under polluted conditions is because vertical velocity drives supersaturation generation; at low supersaturation, when there is very strong competition for water vapor from the many CCN present (velocity-limited regime), any increase in vertical velocity augments supersaturation and the droplet number. For low CCN concentrations, however, supersaturation is high so that fluctuations in aerosol translate to an almost equal response in the droplet number ($\partial N_{\mathrm{d}}/\partial d{N}_{\mathrm{a}}\sim \mathrm{1}$; Fig. 4a); therefore fluctuations in vertical velocity, hence supersaturation, do not affect the droplet number ($\partial N_{\mathrm{d}}/\partial w$ small). The low MBL aerosol concentrations lead to the highest sensitivity of N_d to N_a (approaching 100 %), creating an aerosol-limited condition where there is sufficient available supersaturation to activate virtually every aerosol added to the MBL layer. A $\sim \mathrm{5}\times$ increase in N_a leads to a ∼50 % decrease in the sensitivity of N_d to N_a to around 40 %, with the highest aerosol values corresponding to even lower sensitivities to the aerosol number, approaching below 10 % and demonstrating behavior clearly consistent with a velocity-limited regime.

Predicted droplet sensitivity to κ displays a unique trend (Fig. 4c), becoming stronger initially with increasing aerosol, peaking at intermediate concentrations and then rapidly dropping towards insensitivity, when supersaturation approaches 0.1 %. This sudden insensitivity to CCN activity aligns with the clouds being overseeded when supersaturation is starting to be depleted; once supersaturation is not as readily available, any characteristics of the aerosol cease to play a strong role in activation. However, prior to reaching the point of being insensitive to aerosol, increased sensitivity to κ is opposite to the expected trend from N_a – indicating that the fluctuation in chemical composition, when droplet formation is in a competitive regime (Fig. 4c), may be an important contributor to droplet formation – consistent with the findings of Bougiatioti et al. (2017) for droplet formation in an urban environment in the eastern Mediterranean region. We emphasize here that the sensitivity to κ (Fig. 4c) is not from its changes in terms of size (which we show above to be small) but rather changes over space and time.

Impact of boundary layer turbulence

Throughout the entirety of flights, the maximum predicted droplet number reaches a plateau, where additional aerosol does not result in any significant increase in N_d. An example of this behavior is presented in Fig. S4 (where data of calculated N_d are presented for the entire research flight, as opposed to only the segments in the MBL shown in previous sections). This plateau, owing to the development of strong water vapor limitations, is termed the limiting droplet number, $N_{\mathrm{d}}^{\text{lim}}$, and should largely be a function of vertical velocity – precisely because we are in a velocity-limited regime. This realization implies that much of the droplet number variability (measured or retrieved) in clouds strongly influenced by BB plumes reflects the underlying shifts in cloud dynamics associated with each concentration regime. Indeed, the characteristic velocity in the MBL tends to increase as the MBL clouds become progressively polluted (Fig. 5); the most polluted flights (RF01 and RF02) both fall in mid-August and are coincident with a higher characteristic vertical updraft velocity of ∼0.4, while clean MBL flights coincide with lower vertical-updraft-velocity values of ∼0.3 and occur towards the end of August. Intermediate-scenario flights are divided between the two characteristic vertical updraft velocities observed. When the flight-specific characteristic velocity is then used to calculate the droplet response, it follows a trend with aerosol levels that magnifies droplet response from what is expected by increasingly adding pollution alone. In contrast, the aerosol concentration above the MBL is inversely correlated with w^∗ (Fig. 5), possibly a result of enhanced mixing between the MBL and the free troposphere (rich in BB aerosol) that is associated with the elevated levels of turbulence (w^∗).

Figure 5Characteristic velocity, w^∗, in the MBL as a function of N_a (cm⁻³) in the BBOA plume (blue) and in the MBL (red), for each flight.

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The impact of increased w* on the droplet number is shown for polluted, intermediate and clean conditions in the inset plot of Fig. 6 – which shows $N_{\mathrm{d}}^{\text{lim}}$ for each concentration class for w^∗ between 0.1 and 0.6 ms⁻¹. For polluted conditions, transitioning from 0.3 to 0.4 ms⁻¹ increases the droplet number from 400 to 500 cm⁻³, which is a 20–25 % increase. The enhancement is equally important for intermediate and clean conditions (although less in terms of absolute number) and always comparable to droplet enhancements from changes in BB concentration.

Figure 6$N_{\mathrm{d}}^{\text{lim}}$ (cm⁻³) for each flight as a function of characteristic vertical updraft velocity, w^∗ (ms⁻¹). Flights are colored by polluted, intermediate, and clean categories, as defined by MBL concentration. The inset also presents the asymptotic activated droplet number ($N_{\mathrm{d}}^{\text{lim}}$; cm⁻³) for w^∗ ranging from 0.1 to 0.6 ms⁻¹.

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3.4 Water vapor limitations and the lifetime of BB aerosol in the MBL

Above an aerosol concentration of ∼800 cm⁻³ when water vapor availability is severely limited, N_d no longer increases in response to increases in CCN (Fig. 3a). An important consequence is that, under such conditions, much of the BBOA (biomass burning organic aerosol) does not activate into cloud droplets and is therefore not lost through wet deposition. Because of this, the degree of water vapor competition (and supersaturation level) is directly related to BB lifetime in the MBL. $\partial N_{\mathrm{d}}/\partial d{N}_{\mathrm{a}}$ may then be inversely linked to CCN lifetime, where velocity-limited conditions, characterized by the smallest droplet activation fraction and $\partial N_{\mathrm{d}}/\partial d{N}_{\mathrm{a}}$, also have the largest lifetime and vice versa for clean MBL conditions.