2.1.1 Cloud boundaries

The cloud boundaries at the ARM SGP site were primarily determined by the ARM Active Remotely-Sensed Cloud Locations (ARSCL) product, which is a combination of data detected by multiple active remote-sensing instruments, in particular, the millimeter-wavelength cloud radar (MMCR). The MMCR operates at a frequency of 35 GHz (and wavelength of 8.7 mm) with a zenith-pointing beamwidth of 0.2^∘ and provides a continuous time–height profile of radar reflectivity with temporal and spatial resolutions of 10 s and 45 m, respectively (Clothiaux et al., 2000). After 2011, the MMCR was replaced by the Ka-band ARM zenith radar (KAZR), which has the same operating frequency and shares similar capabilities to the MMCR, but with the major improvement of a new receiver that allows for more sensitivity in cloud detection (Widener et al., 2012). The temporal and vertical resolutions of KAZR-detected reflectivity are 4 s and 30 m, respectively. The cloudy condition, as well as cloud top height, is identified via cloud radar reflectivity. The uncertainties of cloud top height detected by MMCR and KAZR are 45 and 30 m, respectively.

The cloud radar is sensitive to the sixth moment of droplet size distribution and can be contaminated by insects below the cloud base (Dong et al., 2006). The laser ceilometer measurement, which is sensitive to the second moment, is used to provide an accurate cloud base estimation. The uncertainty of cloud base height is around 10 m (Morris, 2016). Hence, the lidar–radar pair provides the most precise determination of cloud boundaries from a point-based perspective, with combined uncertainties of cloud thickness for MMCR and KAZR periods being 55 and 40 m, respectively. Note that this will not cause a significant difference in determining the cloud boundaries between these two radar periods. In this study, the cloud base and top heights were averaged into 5 min bins, where the low-level stratus cloud is defined as a cloud-top height lower than 3 km with no overlying cloud layer (Xi et al., 2010).

2.1.2 Cloud microphysical properties

The cloud liquid water path (LWP), defined as the column-integrated cloud liquid water, was retrieved based on the measured brightness temperatures from the microwave radiometer (MWR) at 23.8 and 31.4 GHz, using the statistical method described in Liljegren et al. (2001). The uncertainty of LWP retrieval is 20 g m⁻² for an LWP less than 200 g m⁻² and around 10 % for an LWP higher than 200 g m⁻². In this study, we exclude the data points with LWPs less than 20 g m⁻² to eliminate optically thin clouds as well as exclude the samples with LWPs greater than 300 g m⁻² to prevent potential precipitation contamination issues (Dong et al., 2008).

For microphysical properties of low-level stratus, following the methods developed by Dong et al. (1998), the daytime layer-mean cloud-droplet effective radius (r_e) can be calculated by

where γ is the solar transmission, μ₀ is the cosine of solar zenith angle, and the units of r_e and the LWP are micrometers (µm) and 100 g m⁻², respectively. N_d is obtained after r_e and is known by the following calculation:

where N_d is number per cubic centimeter (cm⁻³), ΔZ is cloud thickness determined from cloud boundaries with units of meters, and σ_x is the width of the lognormal size distribution of cloud droplets, which is assumed to be a constant value of 0.38 (Miles et al., 2000). The sensitivities of retrieved r_e and N_d to the uncertainties of the cloud LWP, σ_x, and γ have been investigated in Dong et al. (1997, 1998). The uncertainties of retrieved r_e and N_d have been estimated against aircraft in situ measurements over the ARM SGP site (Dong et al., 2002; Dong and Mace, 2003) and other regions (Dong et al., 1998). As a result, the 10 % change in the cloud LWP and downward SW at the surface would cause a 10 % uncertainty in r_e retrieval. In addition, the N_d uncertainty is statistically estimated to be 25 %, compared with the aircraft in situ measurements at the Pennsylvania State University surface site during fall 1996 (Dong et al., 1998) and at the ARM SGP site during the March 2000 cloud intensive observational period (IOP; Dong et al., 2002; Dong and Mace, 2003).

3.3.1 Aerosol absorptive properties of the 16 selected cases

The measured absorptive properties of aerosols can aid in inferring the general information of different aerosol species, since different types of aerosols can demonstrate different absorptive behaviors at certain wavelengths. Aerosol plumes dominated by organic carbonaceous particles tend to represent strong absorptive capabilities in the visible spectrum but weakly absorb in the near-infrared spectrum (Dubovik et al., 2002; Lewis et al., 2008), while black carbon particles (e.g., soot) absorb across the entire solar spectrum with a weak dependence on wavelength (Schuster et al., 2005; Lack and Cappa, 2010). However, when the aerosol plume is dominated by anthropogenic inorganic pollution, the absorbing ability becomes even weaker (Clarke et al., 2007), partly due to sulfate chemical species (Mian Chin et al., 2009). Therefore, the general existence of carbonaceous and pollution particles can be inferred via absorptive properties.

In this study, we adopt the classification method involving the AE and the ratio of the aerosol absorption coefficient to the total extinction coefficient or single-scattering co-albedo, (${\mathit{\omega }}_{\mathrm{abs}}={\mathit{\sigma }}_{\mathrm{abs}}/\left({\mathit{\sigma }}_{\mathrm{abs}}+{\mathit{\sigma }}_{\mathrm{scat}}\right)$) (Logan et al., 2013, 2014). This parameter is more sensitive to the capabilities of aerosol light absorption (rather than scattering) to total aerosol light extinction and therefore can better infer the aerosol composition (Logan et al., 2013). The ω_abs values at a wavelength of 450 nm along with the AE_{450−700 nm} of all the samples are shown in Fig. 4. A ω_abs value of 0.07 is used as a demarcation line of aerosols that are weakly and strongly absorbing. This value was determined using a frequency analysis performed at four AERONET sites that are dominated by single aerosol modes (Logan et al., 2013). Of the 16 cases, six cases are dominated by strongly absorbing aerosols, six cases are dominated by weakly absorbing aerosols, and four cases have samples which broadly scatter across the ω_abs domain, which denotes a mixture of different absorbing aerosol species.

Figure 4Ångström exponent (AE_{450−700 nm}) and single-scattering co-albedo ω_abs450 of all samples (color coded by case). Horizontal dotted line denotes the demarcation of AE${}_{\mathrm{450}-\mathrm{700}\mathrm{nm}}=\mathrm{1}$. Vertical dotted line denotes the demarcation of ω_abs450=0.07.

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Within the 693 selected samples, 360 data points are classified in the weakly absorptive aerosol regime, while the remaining data points are in the strongly absorptive aerosol regime. It is interesting to note that the majority of the winter cases are dominated by weakly absorbing aerosols, while most of the spring cases exhibit a strongly absorbing aerosol dominance, which suggests that the aerosol plumes over the SGP site also have a seasonal dependence. In spring, owing to the upper-level ridge centered over the western Atlantic, the SGP is located at the northwestern edge of the sub-tropical high. Under this synoptic pattern, the SGP is under the influence of relatively frequent southerly transport of the air masses from Central America, which is characterized by strongly absorbing carbonaceous aerosols produced from biomass burning, as well as the moisture transported from the Gulf of Mexico. During the winter, the SGP site experiences the transported air masses from higher latitudes, with less intrusion of air masses from the south (Andrews et al., 2011; Parworth et al., 2015; Logan et al., 2018).

3.3.2 Aerosol and cloud properties under different absorptive regimes

Figure 5a–c show the PDFs of total N_a, N_CCN, and AE for the two absorptive regimes classified by ω_abs. The distributions of N_a from the two absorptive regimes are comparable to one another. The mean N_CCN for the weakly absorptive regime (559 cm⁻³) is larger than that from the strongly absorptive regime (384 cm⁻³), and the occurrence of high N_CCN values (larger than 1000 cm⁻³) is also higher in the weakly absorptive regime. This suggests different responses of CCN concentration to aerosols that have similar magnitudes but different absorptive properties. The AE distributions suggest dominant fine-mode aerosol contributions for both regimes. As for the cloud microphysical property distributions, cloud samples between the two regimes exhibit different characteristics (Fig. 5d–f). The numbers above the bars in LWP distribution (Fig. 5d) for the two absorptive regimes denote the number of data points which will be used in the analysis with the binned LWP in the later sections. Cloud LWPs and r_e values under the strongly absorptive regime have larger values, which contrasts with those under the weakly absorptive regime. On average, the weakly absorbing regime has higher N_d and smaller r_e (374 cm⁻³ and 6.9 µm, respectively) compared to the strongly absorbing regime (214 cm⁻³ and 8.2 µm). Note that the LWPs under the strongly absorptive regime are generally higher than those under the weakly absorptive regime. This LWP difference might be associated with the seasonality of air mass transport over the SGP as discussed in Sect. 3.3.1. Although the seasonality of aerosol distribution and the LWP has similar trends, no clear causality has been found between them. Thus, the question behind these results is whether the differences in cloud microphysical properties between the two regimes are due to the difference in LWPs. As previously stated by Dong et al. (2015), cloud droplets generally grow larger at higher LWPs, which eventually leads to lower droplet number concentration.

Figure 5Aerosol and cloud properties under the strongly absorptive (in red) and the weakly absorptive (in blue) aerosol regimes. PDFs, mean values, and standard deviations of (a) N_a, (b) N_CCN, (c) AE_{450−700 nm}, (d) LWP, (e) N_d, and (f) r_e.

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3.3.3 Relationship of aerosol activating as CCN under different absorptive regimes

The measured N_a and N_CCN under the strongly and weakly absorbing aerosol regimes are plotted in Fig. 6. Note that N_a samples from both regimes cover a broad range of values from 200 to 3500 cm⁻³, suggesting a wide variety of aerosol loading conditions. These highly overlapping distributions allow a quantitative comparison between the ratios of N_CCN to N_a. For a broad range of N_a, especially 200–500 and 1100–3500 cm⁻³, the majority (∼74 %) of sample points from the strongly absorbing regime are located below the samples from the weakly absorbing regime. The linear regressions (95 % confidence level) between N_CCN and N_a for two regimes demonstrate the sensitivity of CCN_0.2 % SS to total aerosol loading. Note that the slope derived from the weak regime is slightly steeper than the strong regime, indicating that the N_CCN values in the weakly absorptive regime increase faster than in the strongly absorptive regime with the same amount of aerosol increment. On average, 54 % of weakly absorbing aerosols can effectively activate as CCN compared to 45 % of the strongly absorbing aerosols. Note that those ratios are computed for an observed supersaturation level of 0.2 %. The fraction of aerosols that can activate as CCN increases with an increase in the supersaturation level, under the same aerosol size and composition condition (Dusek et al., 2006). A sensitivity test of how the aerosol activation ratio varies with different supersaturation levels is done by first interpolating the N_CCN from 0.2 % to 1.15 % and then calculating N_CCN∕N_a. As a result, the ratios of N_CCN∕N_a for the weakly absorptive regime range from 0.54 to 0.38, while the ratios for the strongly absorptive regime range from 0.45 to 0.25. Considering that a supersaturation of 1.15 % in continental boundary-layer stratus is nearly impossible to reach, the supersaturation level of 0.2 % used in this study, which represents the most typical condition for continental low-level stratus, yields reasonable results.

Figure 6Relationship between N_CCN and N_a under the strongly absorptive aerosol regime (in red) and the weakly absorptive aerosol regime (in blue).

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Although it is generally considered that the role of aerosol particle size distribution is more important than the chemical component in terms of becoming CCN (Seinfeld and Pandis, 2012; Dusek et al., 2006), many studies have found that aerosol chemical composition can also have a non-negligible impact on the aerosol activating ability under polluted and low-supersaturation conditions (Rose et al., 2011; Che et al., 2016). According to Köhler theory, the critical level of supersaturation for aerosol activation depends on the aerosol solubility, which decreases with increasing soluble particle number concentration. Hence, the role of aerosol chemical composition is more important at lower supersaturation and diminishes with increasing supersaturation levels (Zhang et al., 2012).

As discussed in Sect. 3.3.1, both weakly and strongly absorptive regimes are linked to aerosol plumes that are dominated by pollution and carbonaceous aerosols, respectively. Therefore, the difference in the ability of aerosol activation between the two regimes can be explained by the different hygroscopicity factors of the particle types. For example, anthropogenic pollution is associated with inorganic particles that are highly hygroscopic and have great ability in taking up water (Hersey et al., 2009; Massling et al., 2009; Liu et al., 2014), while carbonaceous species (e.g., black and organic carbon) exhibit varying degrees of hygroscopicity, with species dominated by hydrophobic soot and black carbon being the least hygroscopic (Shinozuka et al., 2009; Rose et al., 2010). Thus, for the given amount of aerosol loading, aerosols in the weakly absorptive regime can better attract water vapor molecules and result in more aerosol particles activating as CCN.

As shown in Fig. 6, for three N_a ranges (200–500, 500–1100, and 1100–3500 cm⁻³), the strongly absorbing aerosols show different relationships compared to weakly absorbing aerosols. The mean N_CCN∕N_a values for those three N_a ranges for weakly absorptive regimes are 0.77, 0.58, and 0.42, respectively, while the mean N_CCN∕N_a values for the strongly absorptive regimes are 0.35, 0.51, and 0.32, respectively. This phenomenon is due to the mixed effect of aerosol composition (inferred by absorbing ability), aerosol size, and water availability on the aerosol activation. In the 200–500 cm⁻³ N_a range, where the samples from the two absorptive regimes are most separated, the mean values of LWPs (158 g m⁻² and 162 g m⁻² for weakly and strongly absorptive regimes, respectively) indicate relatively sufficient water availability with a smaller aerosol concentration. In addition, the weakly absorbing aerosol sizes are larger (AE=1.59) than the strongly absorbing aerosol (AE=1.73). It is known that larger aerosol particles easily activate under the same composition (Dusek et al., 2006), considering that the weakly absorbing aerosols are more hydrophilic, and thus the largest activation ratio difference among these three ranges is to be expected. The samples in the 500–1100 cm⁻³ N_a range have AE values near 1.40 (1.53) for strongly (weakly) absorbing aerosols. The LWP for the strongly absorptive regime is 167 and 138 g m⁻² in the weakly absorptive regime. Hence, the combined effect of larger particles and more water in the strongly absorptive regime leads to a N_CCN∕N_a ratio close to the N_CCN∕N_a ratio in the weakly absorptive regime. The samples in the 1100–3500 cm⁻³ N_a range exhibit a smaller (AE=1.67 and 1.57 for weakly and strongly absorptive regimes, respectively) aerosol particle size and less water availability (LWP=95 g m⁻² and 127 g m⁻² for weakly and strongly absorptive regimes, respectively), which results in the lowest activation ratio ($N_{\mathrm{CCN}}/N_{\mathrm{a}}=\mathrm{0.42}$ and 0.32 for weakly and strongly absorptive regimes, respectively) among the three ranges for both regimes.

Due to the lack of detailed chemical observations for all the cloud sample periods, as well as the uncertainties among aerosol optical and microphysical properties induced by aerosol transformation processes such as aging and mixing (Wang et al., 2010, 2018b), the bulk activation ratios revealed from this study cannot be significantly distinguished from each other. However, the effect of different aerosol species inferred by the absorptive properties with respect to aerosol activation is evident, especially at the 0.2 % supersaturation level. Furthermore, in the following section, the values of N_CCN∕N_a and AE are sorted by the LWP for the two absorptive regimes in order to rule out the influence of the LWP and AE on aerosol activation.

3.3.4 LWP dependence of aerosol and CCN activation under different absorptive regimes

In order to better understand the role of aerosol activation ability in the microphysical process from aerosol to CCN and then to cloud droplets, comparisons must be considered under similar available moisture conditions due to the discrepancy in the LWP between the two regimes. Accordingly, the sorted N_a values by the stratified LWP are presented in Fig. 7a along with the activation ratios of N_CCN∕N_a, which are denoted by solid lines. For a range of LWPs from 20 to 300 g m⁻², the ratios of N_CCN∕N_a under both regimes increase slightly with an increased LWP. In addition, all binned N_CCN∕N_a values from the weakly absorptive regime (ranging from 0.4 to 0.6) are higher than those from the strongly absorptive regime (ranging from 0.3 to 0.5). A Student's t test is performed to test the ratio difference in each LWP bin at the 95 % significance level. The results indicate that the ratio differences between two absorptive regimes are statistically significant.

Figure 7(a) N_a (dot) and the ratio of N_CCN to N_a (line), (b) N_CCN (dot) and the ratio of N_d to N_CCN (line), (c) r_e, and (d) N_d as a function of LWP under strongly absorptive (in red) and weakly absorptive (in blue) aerosol regimes. Whiskers denote 1 standard deviation for each bin.

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Taking the variation in N_CCN into account, the activation ratios of N_a to N_CCN under low-LWP conditions (<50 g m⁻²) in both regimes could be simply due to the linear combination of high aerosol concentration and insufficient moisture supply such that aerosols are competing against each other, thus resulting in a low activation ratio. However, as the LWP increases, the activation ratios tend to increase as well, especially at LWP values higher than 100 g m⁻². In fact, the values of N_a in both regimes are relatively small, with little variation for LWP>100 g m⁻², while the N_CCN∕N_a ratio demonstrates a more noticeable increasing trend in the weakly absorptive regime. Despite higher aerosol loading in the strongly absorptive regime at large LWPs, there are still more weakly absorbing aerosols being activated, which corresponds to greater water uptake ability. Moreover, in every LWP bin, the AE value for the weakly absorptive regime is either higher than or very close to the AE value for the strongly absorptive regime (figure not shown). Even with relatively smaller particle sizes, under similar water availability, the weakly absorbing aerosol can better activate as CCN. In conclusion, a significant impact of aerosol composition on aerosol activation capacity, which is inferred by the aerosol absorbing capability, does exist.

As for the process from CCN to cloud droplets, a similar assessment is presented in Fig. 7b, which illustrates the N_CCN values and the ratios of N_d∕N_CCN in relation to the LWP. The ratios of N_d∕N_CCN in the weakly absorptive regime range from 0.58 to 0.86, with a mean value of 0.69, and highly fluctuate with the LWP. In contrast, N_d∕N_CCN in the strongly absorptive regime shows lower values and less variability (from 0.47 to 0.64), with a mean value of 0.54. It is interesting to note that the variation in N_d∕N_CCN in the strongly absorptive regime mimics the variation in N_CCN with the LWP, indicating a relatively lower aerosol-to-CCN activating capacity. Therefore, N_d∕N_CCN shows no significant dependence on the LWP, which is consistent with previous studies, which suggest that the response of N_d to the change in N_CCN has no fundamental relationship with the LWP (e.g., McComiskey et al., 2009). In addition, the sensitivity and uncertainty of N_d are examined in order to estimate the impact of N_d uncertainty on the assessment of N_d∕N_CCN. To assess the contributions of different input parameter uncertainties to N_d retrieval, every input parameter was perturbed by its own uncertainty, with other parameters held fixed. The results are as follows: (a) an increase (decrease) in the LWP of 20 g m⁻² leads to 27.9 % (27.6 %) change in N_d while an increase (decrease) in σ_x of 0.15 leads to a 50.8 % (23.9 %) change in N_d, (b) an increase (decrease) in cloud thickness of 0.15 leads to a 14.5 % (23.2 %) change in N_d, and (c) an increase (decrease) in r_e of 10 % leads to a 14.5 % (23.2 %) change in N_d. The percentage changes in N_d due to different input uncertainties range from 14.5 % to 50.8 %, with the majority falling between 20 % and 30 %. Note that the largest uncertainty of N_d happens when increasing σ_x by 0.15. However, when considering that continental stratocumulus generally contains smaller droplets, one might expect their distribution width to be smaller than 0.38 (Dong et al., 1997). Therefore, the overall uncertainty of 25 % compared to the aircraft in situ measurement should be a reasonable estimation. In this case, the mean ratio of N_d∕N_CCN for the weakly absorptive aerosol regime ranges from 52 % to 86 %, while the mean ratio of N_d∕N_CCN for the strongly absorptive aerosol regime ranges from 41 % to 67 %.

The overall differences in CCN conversion fractions are likely a result of the combined effects of meteorological factors and the aerosol heating effect on the cloud environment. To examine the meteorological influence on CCN conversion, the LTS parameter is used to investigate the difference in the large-scale thermodynamic condition. By sorting the LTS by the LWP for the two absorptive regimes, the LWP dependence on LTS can be ruled out, which can provide a better understanding of the potential role of LTS in cloud-droplet development. For each given LWP bin, the weakly absorptive regime has higher LTS values than the strongly absorptive regime (figure not shown). The LTS is largely impacted by the potential temperature difference throughout the mixed layer. If a strong capping boundary-layer temperature inversion is present, it will result in high LTS values and, in turn, a well-mixed boundary layer (Wood and Bretherton, 2006). Such results indicate that even under similar available moisture conditions, the more sufficient turbulence can transport the below-cloud moisture as well as the CCN that activated from weakly absorbing aerosols into the cloud more efficiently, contributing to a higher ratio of N_d∕N_CCN in the weakly absorptive regime. However, the LTS emphasizes a general thermodynamic condition in the lower troposphere with a wider domain as compared to the single-point measurement.

In addition, the vertical velocity in pressure coordinate (ω) values at the 925 hPa level, which represent the large-scale forcing on the vertical motion between surface and cloud layer, are also sorted by the LWP for the two absorptive regimes in order to check the potential influence of the environmental dynamic state (figure not shown). However, the ω values for both absorptive regimes share the same mean value of 0.031 Pa s⁻¹ and show no dependence on the LWP, indicating that the large-scale environments over the SGP are generally dominated by sinking motion. The synoptic patterns of composite geopotential height for the two absorptive regimes show that the ARM SGP site is located ahead of the 700 hPa ridge and is located within the surface high pressure. The meteorological pattern is favorable for the generation of downward motion at the lower troposphere, and the sinking motion induces relatively stable environments in the lower troposphere, which is consistent with the LTS measurements. Considering the fact that the ω value is obtained from a relatively larger domain surrounding the SGP, it is difficult to reflect the true cloud-scale dynamics, especially the vertical velocity or turbulence strength at the cloud base. Therefore, the influence of cloud-scale dynamics, presumably cloud-base updraft, is non-negligible, since the sensitivity of cloud droplet to aerosol loading is enhanced with increasing updraft velocity as reported in previous studies (e.g., Feingold et al., 2003; McComiskey et al., 2009).

Furthermore, the heating effect of light-absorbing aerosols on the cloud environment cannot be neglected. Strongly light-absorbing aerosols can absorb solar radiation and heat the in-cloud atmosphere by emission, which results in the reduction of relative humidity (or supersaturation) in the cloud layer (Bond et al., 2013; Wang et al., 2013b). This effect is evident in the observation, as the values of in-cloud relative humidity in the strongly absorptive regime are slightly lower than those in the weakly absorptive regime. Additionally, this aerosol heating effect disrupts the boundary-layer temperature structure by enhanced warming aloft and, consequently, inhibits the vertical transport of sensible and latent heat between surface and cloud layer. The impacts of light-absorbing aerosol on cloud-scale thermodynamics and the dynamics state might eventually dampen the conversion process from CCN to cloud droplets. Unfortunately, due to the lack of measurement of cloud-base vertical velocity throughout the studying period, this competing effect of cloud thermodynamic and dynamic cannot be fully untangled from the aerosol effect given the currently available dataset. The differences in N_d∕N_CCN between the two regimes might be affected by the combined effects of LTS, updraft velocity, and aerosol heating on the cloud environment.

3.3.5 r_e and N_d dependence on LWP under different absorptive regimes

In the previous section, we examined the activation ratios of aerosol to CCN and then from CCN to cloud droplets between the two regimes as well as their dependencies on the LWP, which eventually led to the cloud-droplet variation for a given LWP range. Figure 7c and d demonstrate that r_e increases while N_d decreases with an increased LWP up to roughly 150 g m⁻² in both regimes. Note that as LWPs are greater than 150 g m⁻², N_d values in both regimes show less variation with the LWP, while r_e values in the strongly absorptive regime also show little variation, which implies limited growth even with increasing water availability. However, the r_e values in the weakly absorptive regime increase from 7.8 to 8.8 µm, which suggests that under a given number concentration, the cloud droplet can grow by continuing to collect moisture. As shown in each LWP bin, the r_e values in the weakly absorptive regime are smaller than those in the strongly absorptive regime, while the N_d values in the strongly absorptive regime are much lower than those in the weakly absorptive regime.

The combination of cloud thermodynamic, dynamic, and light-absorbing aerosol heating effects impact the conversion process from CCN to cloud droplets. Under a given moisture availability, a higher number of CCN in the weakly absorptive regime can be converted to cloud droplets. This results in higher number concentrations of smaller cloud droplets, while the dampened CCN conversion process in the strongly absorptive regime leads to fewer and larger cloud droplets at a fixed LWP.

3.3.6 Aerosol–cloud interaction under different absorptive regimes

To examine the sensitivity of clouds to both weakly and strongly absorbing aerosol loading, the relationships between cloud r_e and N_CCN are shown in Fig. 8. Two LWP ranges (0–50 and 200–250 g m⁻²) are selected in order to better represent ACI_r at low- and high-LWP conditions. For the examination of r_e as a function of N_CCN (Fig. 8a for low LWP range), the ACI_r values in the weakly absorptive regime are higher than those in the strongly absorptive regime. This suggests that the cloud droplets are more sensitive to weakly absorbing aerosols than to strongly absorbing aerosols in clouds with low LWPs. In other words, if there is some increment in aerosol particles, clouds influenced by weakly absorbing aerosols will respond to this increment more effectively and decrease faster in droplet sizes relatively. Under high-LWP conditions (Fig. 8b), the ACI_r values are lower and show less difference between the two regimes, which is in agreement with previous discussions on the sensitivity of cloud microphysical properties to aerosol loading.

Figure 8r_e as a function of N_CCN and the values of ACI_r under the strongly absorptive (in red) and the weakly absorptive (in blue) aerosol regimes at two LWP bins: 0–50 g m⁻² (a) and 200–250 g m⁻² (b). Note that the dashed lines denote the uncertainties of ACI_r due to 10 % error in r_e retrieval.

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Based on the sensitivity study, the 10 % change of the cloud LWP and downward SW at the surface would result in the 10 % uncertainty in r_e retrieval (Dong et al., 1997). When compared with aircraft in situ measurements, the differences between retrievals and in situ measurements are around 10 % (Dong et al., 1998, 2002). In order to assess the impact of r_e uncertainty on ACI_r, we use the Monte Carlo method to propagate the r_e uncertainty on ACI_r by the following procedure. The r_e value for each data point is randomly perturbed by ±10 %, and thus the corresponding ACI_r can be recalculated based on the perturbed set of r_e. After 100 000 iterations, we obtain a distribution of ACI_r values. The uncertainty of ACI_r is given by 1 standard deviation of those 100 000 values of ACI_r; since the distribution of ACI_r values follows a normal distribution with a narrow peak, this uncertainty value represents the uncertainty in the computed ACI_r due to errors in the r_e retrieval. The uncertainties of ACI_r for the two absorptive regimes are denoted as the dashed line in Fig. 8. In the lower LWP range (Fig. 8a), the ACI_r uncertainty is 0.020 (0.030) for the weakly (strongly) absorptive regime; to account for the uncertainties, the difference in ACI_r between the two absorptive regimes is preserved. In the higher LWP range (Fig. 8b), the ACI_r uncertainty is 0.044 (0.023) for the weakly (strongly) absorptive regime, which is non-negligible. Taking the uncertainties of ACI_r into account, the ACI_r in the two absorptive regimes cannot be separated well, owing to the enhanced condensational growth process accompanied by a higher LWP and the diminished cloud response to aerosols associated with different ω_abs values. In general, the 10 % uncertainty in r_e retrieval contributes to 0.02–0.04 in ACI_r uncertainties.

Note that the LTS values from the weakly absorptive regime (22.91 and 19.78 K) are higher than those from the strongly absorptive regime (21.72 and 17.83 K) for the selected two LWP bins. As discussed in the previous section, owing to the stronger temperature inversion indicated by the higher LTS values, low clouds are more closely connected to weakly absorbing aerosols and moisture below cloud by sufficient turbulence. In order to quantify the impact of LTS on ACI_r, we adapted the criteria described in Grysperdt et al. (2016) in which the LTS value of 18 K denotes the demarcation line between high- and low-LTS regimes and constrains the ACI_r for the two regimes by their LTS values accordingly. Owing to the highly limited sample points that fall into the low-LTS category, the ACI_r can only be constrained in the high-LTS condition. For the 0–50 g m⁻² LWP range, the ACI_r for the weakly absorptive regime increases from 0.26 to 0.31, and the ACI_r for the strongly absorptive regime increases from 0.21 to 0.24. The enhancement effect of LTS on the ACI_r is noticeable, which is in accordance with the previous discussion in which the high-LTS environment is associated with (a) sufficient turbulence in the boundary layer and (b) a closer connection between the surface and cloud layer, which enhances the cloud microphysical responses to the CCN. The result is consistent with the previous study by Kim et al. (2008), who found that ACI_r is enhanced under adiabatic cloud conditions and higher LTS values are associated with higher cloud adiabaticity. Note that though ACI_r values are increased for both regimes, the difference between the regimes becomes larger (from 0.05 to 0.07) because low-level stratus clouds are more susceptible to weakly absorptive aerosol. Furthermore, the enhancement of ACI_r in the high-LTS environment is more evident in the weakly absorptive regime. In the case of the 200–250 g m⁻² LWP range, the LTS effects on ACI_r are less significant compared to the lower LWP range. No significant change in the weakly absorptive regime is evident, and the ACI_r in the strongly absorptive regime decreased from 0.12 to 0.10, partly owing to the enhanced condensational growth process accompanied by a higher LWP, and thus inhibits the impact of LTS on ACI_r. Overall, ACI_r values are enhanced under high-LTS conditions, but the difference between the two regimes indicates that both ω_abs and LTS can be the impact factor of the ACI_r, but there is not necessarily causality between them.

Furthermore, with the presence of strongly light-absorbing aerosols, the cloud-layer heating induced by the aerosol absorptive effect can result in the reduction of in-cloud supersaturation and leads to the damping of cloud microphysical sensitivity to strongly absorbing aerosols. A previous modeling study conducted at the ARM SGP site by Lin et al. (2016) estimated the shortwave heating rates in cloud layers by contrasting the simulations with and without light-absorbing aerosols. The inclusion of light-absorbing aerosols was represented by an internal aerosol mixture with a mass combination of 95 % ammonium sulfate and 5 % black carbon. The SSA of this mixture is calculated to be roughly 0.9, as documented in the previous study of Wang et al. (2014). The different values of SSA used in their study (0.9 for light-absorbing and 1.0 for non-absorbing) are comparable to this study (0.89 for strongly absorbing and 0.97 for weakly absorbing). The induced increments in cloud-layer shortwave heating rates have a maximum value of 3 K d⁻¹, compared to the simulation without light-absorbing aerosols. Note that the aerosol number concentration in Lin et al. (2016) was set to 2800 cm⁻³. To get a simple comparison with the aerosol number concentration in this study, one might expect that the light-absorbing-aerosol-induced cloud-layer shortwave heating rates can have a similar maximum increment, and the general increment should be about 1 K d⁻¹, which is non-negligible. The absorption of solar radiation by light-absorbing aerosols warms the cloud layer as well as the boundary layer below it, which in turn stabilizes the lower troposphere and results in reduced cloud susceptibility. In general, the results indicate that the ACI_r can be counteracted by the light-absorbing aerosol heating effect and be enhanced under a thermodynamic environment of high static stability, especially under lower-LWP conditions.