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. 2018 Jun 13;45(11):5771–5778. doi: 10.1029/2018GL077562

Opposite Aerosol Index‐Cloud Droplet Effective Radius Correlations Over Major Industrial Regions and Their Adjacent Oceans

X Ma 1,✉, H Jia 1, F Yu 2, J Quaas 3
PMCID: PMC6049888  PMID: 30034046

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 and the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim reanalysis data from 2003 to 2016 are employed to study aerosol‐cloud correlations over three industrial regions and their adjacent oceans, as well as explore the impact of meteorological conditions on the correlations. The analysis focusing on liquid and single‐layer clouds indicates an opposite aerosol‐cloud correlation between land and ocean; namely, cloud effective radius is positively correlated with aerosol index over industrial regions (positive slopes), but negatively correlated over their adjacent oceans (negative slopes), for a quasi‐constant liquid water path. The positive slopes are relatively large under low lower‐tropospheric stability (LTS; weakly stable condition), but much weaker or even become negative under high LTS (stable conditions) and high liquid water path. The occurrence frequency of cloud top height (CTH) and LTS suggests that positive correlations are more likely corresponding to relatively high CTH and low LTS, while negative to low CTH and high LTS.

Keywords: aerosol‐cloud correlation, MODIS, ERA‐Interim reanalysis, cloud effective radius, aerosol index, lower‐tropospheric stability

Key Points

  • Cloud effective radius is positively correlated to aerosol index over three industrial regions, but negatively over adjacent oceans

  • Positive slopes over land are relatively large under low lower‐tropospheric stability (LTS), but much weaker or negative under high LTS

  • Positive correlations possibly correspond to relatively high cloud top height (CTH) and low LTS, while negative to low CTH and high LTS

1. Introduction

Aerosol particle concentrations have increased dramatically since preindustrial due to anthropogenic emissions, particularly over northern industrial regions. They influence climate change by directly scattering and absorbing incoming solar radiation and indirectly acting as cloud condensation nuclei (CCN), altering cloud properties, and precipitation (Albrecht, 1989; Twomey, 1977). According to Twomey (1977), the increase of aerosol particles results in more and, for a constant cloud liquid water path (LWP), smaller cloud droplets, that is, cloud effective radius (CER) decreases with increasing of aerosol concentrations. Many previous studies have confirmed this negative relationship between aerosol number and CER, either from aircraft observations (Kleinman et al., 2012; Pawlowska & Brenguier, 2000; Roberts et al., 2008; Werner et al., 2014; Wilcox et al., 2006) or satellite data (Bréon et al., 2002; Chen et al., 2014; Christensen et al., 2016; Costantino & Bréon, 2010, 2013; Kaufman et al., 2005; Koren et al., 2005, 2010; Lihavainen et al., 2010; Nakajima et al., 2001; Quaas et al., 2008; Zhu et al., 2015).

In recent years, however, quite a few studies found that this relation is not always true; instead, the negative correlation between aerosol particles and CER is not found distinctly or could be even positive; that is, CER increases with aerosol number (Bulgin et al., 2008; Grandey & Stier, 2010; Liu et al., 2017; Sekiguchi et al., 2003; Tang et al., 2014; Wang et al., 2014, 2015; Yuan et al., 2008). It is acknowledged that aerosol‐cloud interactions are quite complex processes, influenced by both aerosol microphysics (size and chemical composition) and meteorological conditions. The studies by Dusek et al. (2006) and Zhang et al. (2011) found that particle size is dominant to determine aerosol activation, but many studies demonstrated that the chemical composition is also critical (Almeida et al., 2014; Ervens et al., 2005; Lance et al., 2004; McFiggans et al., 2006; Nenes et al., 2002; Wang et al., 2008). In addition, dynamical or meteorological conditions will strongly affect aerosol‐cloud interactions by changing vertical velocity or wind shear (Gryspeerdt et al., 2014; Fan et al., 2009; Koren et al., 2010; Loeb & Schuster, 2008; Mauger & Norris, 2007; Saponaro et al., 2017; Su et al., 2010; Tang et al., 2014; Wang et al., 2014). Overall, negative correlations are normally found over oceans while positive over land. Yuan et al. (2008) explored the positive correlation using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products and speculated that a positive correlation is possibly due to slightly soluble organics particles, which induced a decrease of aerosol activation. Wang et al. (2014) investigated aerosol‐cloud interaction over Eastern China by using both MODIS satellite and National Centers for Environmental Prediction reanalysis data and concluded that negative correlations are usually found when atmospheric stability is relatively high while positive correlations are more likely to be found when the atmosphere is relatively unstable. Some recent studies found that the correlation is probably also related to cloud type; for example, Chen et al. (2016) employed CloudSat satellite and ERA‐Interim reanalysis data to identify the impact of aerosols on the vertical development of clouds and found out that aerosols inhibit the cloud development for shallow cumulus clouds, while enhance deep cumulus, nimbostratus, and deep convective clouds. Gryspeerdt and Stier (2012) found that the strongest positive correlation occurs in the stratiform cloud regimes, while the negative one is analyzed for the shallow cumulus cloud regimes. For the northern industrial regions, anthropogenic emissions are much higher than over other continental regions; thus, how aerosols impact cloud formation and development and further climate change over such regions is a vital question to address, but substantial uncertainties still remain.

In this study, we chose three regions with strong anthropogenic emissions in the Northern Hemisphere, namely, East China (EC), East U.S. (EU), and West Europe (WE), and their adjacent oceans (ECO, EUO, and WEO), to systematically explore the aerosol‐cloud correlations over land with strong anthropogenic emissions and over relatively clean ocean by using both MODIS satellite data and ERA‐Interim reanalysis data. The paper is organized as follows: the data descriptions of both satellite and meteorology and data processing are presented in section 2; the aerosol‐cloud correlations over the study regions are discussed in section 3. Possible explanations and discussions are summarized in section 4.

2. Data

The data used in this study include the MODIS Collection 6 Level 3 (C6 L3) product, which provides aerosol (Levy et al., 2013) and cloud properties (Platnick et al., 2017) at 1° × 1° spatial resolution and daily time resolution, as well as European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim reanalysis data, which provides meteorological fields at spatial resolution from 0.125° to 2.5° and at 4 times (00, 06, 12, and 18 UTC) every day (Dee et al., 2011). The total 14 years (2003–2016) of these data are used for analysis.

2.1. MODIS

Although it is essential to have coincidental aerosol and cloud properties in order to examine their correlations, many satellite retrievals do not provide such data since aerosol retrievals are usually conducted when cloudy conditions are excluded. However, studies such as Bréon et al. (2002), Anderson et al. (2003), and Avey et al. (2007) showed that in the case of daily products at 1° × 1° resolution, it is possibly unnecessary to individually couple the aerosol and cloud measurements. Therefore, aerosol and cloud data retrieved from cloudy and clear, respectively, pixels within a 1° × 1° in this study are assumed to be co‐located. The MODIS C6 L3 product includes cloud microphysical parameters (CER, LWP, and cloud optical depth) with statistics (mean, minimum, maximum, and standard deviation) determined at three different wavelengths (1.6, 2.1, and 3.7 μm) for each cloud phase (liquid, ice, and undetermined) separately. We filtered the MODIS cloud data according to the criteria employed by Saponaro et al. (2017) to make sure the data used are only for liquid phase and single layer clouds, and also for nonprecipitating cases. The daily AOD product and cloud parameters from January 2003 to December 2016 were employed for the statistics in order to obtain statistically significant results from the samples large enough.

2.2. Meteorology

To assess the effect of meteorological conditions on cloud properties, the ECMWF ERA‐Interim reanalysis data were applied to derive the lower‐tropospheric stability (LTS). The LTS is computed as the difference between the potential temperature at 700 hPa and at the surface (Klein & Hartmann, 1993), describing the magnitude of the inversion strength for the lower troposphere.

3. Aerosol‐Cloud Correlations

3.1. Aerosol Index and Cloud Properties

Aerosol index (AI), in comparison to aerosol optical depth (AOD), is believed to be a better parameter to represent aerosol when examining its correlation with cloud, because it includes the information of aerosol size, which is critical for cloud formation and properties (Penner et al., 2011; Stier, 2016). The AI is used as a proxy for the fine mode aerosol particles, which have a larger contribution to the CCN than the coarse‐mode particles (Nakajima et al., 2001). The AI is derived based on AOD and Ångström exponent; the former is provided by the MODIS C6 product, and the latter is calculated from AOD at wavelength 460 and 660 nm.

According to Twomey, more but, for constant liquid water content, smaller cloud droplets form as aerosol particle concentrations increase. Both AI and CER here are therefore stratified according to LWP in order to examine the correlations between CER and AI at smaller range of LWP (quasi‐constant LWP). CER varies with AI over land, and ocean at various cloud top layer (650–750 hPa, 750–850 hPa, 850–950 hPa, and 650–950 hPa; Figure S1) shows that CER decreases with increasing AI over ocean, but CER increases with AI over land. The positive correlations can be found at various vertical levels, which is most distinct at smaller LWP and weaken with the increase of LWP. The computed slopes from linear regressions of CER versus AI on log‐log scale with LWP‐stratified for the layer 650–950 hPa over three anthropogenic regions and their adjacent oceans are summarized in Figure 1, which clearly show that for a given LWP, slopes are normally negative over ocean but positive over land. The opposite correlation over land has been reported, though not explained, in a few previous studies (Grandey & Stier, 2010; Liu et al., 2017; Tang et al., 2014; Wang et al., 2014, 2015; Yuan et al., 2008). It is interesting to see that as LWP increases, the positive slopes over land become weaker while negative slopes over ocean change slightly. The associated statistics listed in Table 1 demonstrated that the slopes of the regression over land are in all cases—but four (both for 140–180 and 180–220 g/m2)—positive at a statistically significant level (according to a Student's t test, α = 0.05). It is argued that the MODIS retrieval of Ångström exponent over land is problematic (Sayer et al., 2013); thus, a similar analysis as discussed above but using AOD as aerosol proxy are conducted, which is shown in Figure S2 and Table S1. It is clearly demonstrated that the statistical results, particularly the signs and magnitudes of slopes, though change slightly, do not influence our conclusions.

Figure 1.

Figure 1

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The computed slopes of CER versus AI on log‐log scale, in which both CER and AI are stratified according to LWP.

Table 1.

The statistics in Figure 1

LWP (g m−2) Region 20–60 60–100 100–140 140–180 180–220
ECO R 2 t R 2 t R 2 t R 2 t R 2 t
0.32 ○ 0.33 ○ 0.56 ○ 0.43 ○ 0.62 ○
EUO R 2 t R 2 t R 2 t R 2 t R 2 t
0.01 × 0.55 ○ 0.58 ○ 0.49 ○ 0.75 ○
WEO R 2 t R 2 t R 2 t R 2 t R 2 t
0.16 × 0.76 ○ 0.89 ○ 0.89 ○ 0.90 ○
EC R 2 t R 2 t R 2 t R 2 t R 2 t
0.74 ○ 0.62 ○ 0.41 ○ 0.33 ○ 0.41 ×
EU R 2 t R 2 t R 2 t R 2 t R 2 t
0.91 ○ 0.90 ○ 0.90 ○ 0.40 ○ 0.12 ×
WE R 2 t R 2 t R 2 t R 2 t R 2 t
0.85 ○ 0.83 ○ 0.54 ○ 0.04 × 0.13 ×
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Note. The coefficients of determination (R 2) is listed, and the cases at a statistically significant level (according to a Student's t test, α = 0.05) are represented in circles, otherwise in crosses.

The cloud properties as a function of cloud top pressure (CTP) are presented in Figure 2, in which all cloud data are grouped into five classes according to AI. The groups are classified to make sure that each group has roughly same number of the samples. Koren et al. (2005) previously suggested an enhancement of cloud vertical structure over the North Atlantic due to aerosol loading. Saponaro et al. (2017) also found that the lowest CTP corresponds to the highest classes of AI over the Baltic Sea region. This is not evident over three anthropogenic regions but seems true over the adjacent oceans (Figure 2), especially ECO and EUO, where the lowest CTP values (highest cloud top height, CTH) correspond to highest classes of AI. The highest cloud fraction (CF) is overall consistent with the highest classes of AI, possibly indicating an enhancement of cloud formation due to aerosols, which is evident over all three adjacent oceans. An alternative explanation is covariation of CF and AI, both responding to relative humidity variability (Gryspeerdt et al., 2016; Quaas et al., 2010). Over land, however, although CF increases with AI over three continents, it is more evident over EC. LWP exhibits the opposite relationship with AI, that is, LWP increases with AI over WE while it decreases over EC. Over ocean, LWP, in general, is smaller when AI increases, which is more evident over ECO and EUO, possibly because large cloud droplets more likely become to rain droplet and thus reduce the LWP. The LWP remains rather constant over WEO, where both CER and LWP are much smaller than in the other two regions.

Figure 2.

Figure 2

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(top row) Cloud fraction (CF) and (bottom row) liquid water path (LWP) as a function of cloud top pressure (CTP) over the three regions. Cloud properties are grouped into five classes according to AI.

3.2. Impacts of Thermodynamic and Dynamic Parameters on the Correlations

Thermodynamic and dynamic conditions are important on vertical transport of aerosol, activation process, and aerosol‐cloud interaction. LTS is often used to represent the large‐scale thermodynamic conditions. We computed LTS from the difference of potential temperature between 700 hPa and sea level according to the definition by Klein and Hartmann (1993). The slopes of CER versus AI on log‐log scale under low, medium, and high LTS conditions, and stratified by different classes of LWPs, are shown in Figure 3a. The three LTS levels have the same number of samples (Table S2). It is obvious that positive slopes under low LTS (weak stable condition) are relatively large compared to medium (medium stable) and high LTS (stable condition). It is also noted that positive slopes could be much weaker or even become negative under high LTS and when LWP are relatively high. This may imply that when the lower atmosphere is very stable, an accumulation of aerosol particles induces a rapid increase of aerosol size and thus more likely to activate as CCN, in which water vapor is exhausted very quickly for both aerosol hygroscopic growth and cloud droplet formation. This may induce a large number of small cloud droplet and thus higher LWP. Figure 3b confirmed that more CF was found when the lower layer atmosphere is very stable (high LTS), which is discerned over three anthropogenic regions. Overall, the negative slopes over ocean are consistent at low, medium, and high LTS. We conducted the statistical analysis by stratifying the data according to three categories of vertical velocity at 850 hPa (ω), that is, updraft, weak vertical velocity, and downdraft (Table S3), to test the impact of dynamic conditions on the correlations, and the results do not show evident dependence of the correlations on ω (Figure S3).

Figure 3.

Figure 3

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(a) The computed slopes of CER versus AI on log‐log scale at (left) low, (middle) medium, and (right) high LTS conditions, where all data are stratified by LWP. The white box represents NA; (b) CTP varies with CF and LWP under low, medium, and high LTS conditions.

It is known that aerosol emission and its microphysical properties such as chemical composition, size, and mixing state could have strong seasonal variability, which possibly influences the process of aerosol activation and thus CER versus AI correlations (Bhattu & Tripathi, 2015; Crosbie et al., 2015; McFiggans et al., 2006; Meng et al., 2014; Padró et al., 2012; Paramonov et al., 2013; Petters & Kreidenweis, 2007; Topping & McFiggans, 2012; Q. Zhang et al., 2012; Zhang et al., 2016). Unfortunately, there are only station‐based observational data available for aerosol microphysics, it is impossible currently to obtain aerosol chemical component, size, and mixing state on a regional scale. As aerosol microphysics exhibit significant seasonal variations, it is thus useful to look into the correlations in other seasons. In comparison to summer season, the slopes in winter exhibit some yet insignificant differences (figure omitted). The negative slopes over ocean are generally unchanged; however, the positive slopes found in summer over land are less pronounced in winter; in some cases, for example, while LWPs are relatively high, slopes could become negative.

4. Summary and Discussions

It should be pointed out that CER‐AI (AOD) correlations can result from both real physical‐chemical processes and artificial correlations in light of various data uncertainties. Although we apply very strict screening criterion in selecting the data to avoid this issue as much as possible (see section 2.1), it is still possible that the correlation is veiled by various artifacts. The potential artifact includes cloud contamination, cloud 3‐D effect, aerosol humidity, and dynamic effect. Previous studies (e.g., Yuan et al., 2008) assessed the potential effect of partial cloud and concluded that cloud contamination is not likely the cause of the observed positive correlation between AOD and CER. It is found that 3‐D effects tend to have stronger impact on CER at 2.1 μm (CER,2.1) than CER at 3.7 (CER,3.7; Zhang & Platnick, 2011; Z. Zhang et al., 2012). We apply CER,3.7, rather than CER,2.1, to conduct the similar analysis, and the results (Figure S4 and Table S4) show a slight difference, so it seems that cloud 3‐D effect might not influence our conclusion significantly. Although quite a few previous studies have attempted to explore what factors contribute to such positive values as stated earlier, they often apply to some conditions but not working for others (Tang et al., 2014; Wang et al., 2014; Yuan et al., 2008).

As presented above, positive slopes occur over three industrial regions, with the highest values over EC and EU during the summer seasons, while positive correlations become less pronounced during winter season and over WE. As shown in Figure 2 the CTP over three continental regions are overall lower than 600 hPa, with the lowest of 500 hPa over EC, while CTP over the adjacent oceans are typically higher than 600 hPa, with the highest of 650 hPa over WE. The results in winter also show the significant variations of CTP. The occurrence frequency of CTH during the entire study period in summer and winter presents a significant difference between the continental and their adjacent oceans, and the distinct regional differences as well (Figure 4). In summer, the occurrence of low clouds over ocean is most frequent, especially over ECO and EUO, where 84% clouds occur at lower than 2 km. On contrast, more clouds occur with CTH higher than 2 km over the industrial regions. The smallest difference of CTH between land and ocean is found over WE. In winter, the differences of CTH between land and ocean are much smaller, but we can still found that the higher clouds over land occur more frequently than ocean.

Figure 4.

Figure 4

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The occurrence frequency of cloud top height (CTH) and lower troposphere stability (LTS) over the study regions in summer and winter.

As expected, the frequencies of the cloud under lower LTS (unstable) are higher over land than over ocean. Also, unstable clouds occur more frequently in summer than in winter, and the differences of frequencies between land and ocean are very small in winter. Therefore, it is suggested that positive correlations are more likely occur when clouds are relatively high and unstable, while negative correlations normally occur when clouds are relatively low and stable. We hypothesize that this might be associated with collision/coalescence and/or entrainment mixing processes. More studies on the physical mechanisms will be conducted in subsequent research.

Supporting information

Supporting Information S1

Click here for additional data file. (5.5MB, docx)

Acknowledgments

This study is supported by the National Natural Science Foundation of China grants (41475005 and 41675004). F. Yu acknowledges funding support from the National Aeronautics and Space Administration (NASA NNX17AG35G). The MODIS Level 3 Collection 6 data were obtained from https://ladsweb.nascom.nasa.gov, and the ECMWF ERA Interim data were collected from the server http://www.ecmwf.int/en/research/climate‐reanalysis.

Ma, X. , Jia, H. , Yu, F. , & Quaas, J. (2018). Opposite aerosol index‐cloud droplet effective radius correlations over major industrial regions and their adjacent oceans. Geophysical Research Letters, 45, 5771–5778. 10.1029/2018GL077562

References

  1. Albrecht, B. A. (1989). Aerosols, cloud microphysics, and fractional cloudiness. Science, 245(4923), 1227–1230. 10.1126/science.245.4923.1227 [DOI] [PubMed] [Google Scholar]
  2. Almeida, G. P. , Brito, J. , Morales, C. A. , Andrade, M. D. F. , & Artaxo, P. (2014). Measured and modelled cloud condensation nuclei (CCN) concentration in São Paulo, Brazil: The importance of aerosol size‐resolved chemical composition on CCN concentration prediction. Atmospheric Chemistry and Physics, 14(14), 7559–7572. 10.5194/acp-14-7559-2014 [DOI] [Google Scholar]
  3. Anderson, T. L. , Charlson, R. J. , Winker, D. M. , Ogren, J. A. , & Holmén, K. (2003). Mesoscale variations of tropospheric aerosols. Journal of the Atmospheric Sciences, 60(1), 119–136. 10.1175/1520-0469(2003)060%3C0119:MVOTA%3E2.0.CO;2 [DOI] [Google Scholar]
  4. Avey, L. , Garrett, T. J. , & Stohl, A. (2007). Evaluation of the aerosol indirect effect using satellite, tracer transport model, and aircraft data from the International Consortium for Atmospheric Research on Transport and Transformation. Journal of Geophysical Research, 112, D10S33 10.1029/2006JD007581 [DOI] [Google Scholar]
  5. Bhattu, D. , & Tripathi, S. N. (2015). CCN closure study: Effects of aerosol chemical composition and mixing state. Journal of Geophysical Research: Atmospheres, 120, 766–783. 10.1002/2014JD021978 [DOI] [Google Scholar]
  6. Bréon, F. M. , Tanré, D. , & Generoso, S. (2002). Aerosol effect on cloud droplet size monitored from satellite. Science, 295(5556), 834–838. 10.1126/science.1066434 [DOI] [PubMed] [Google Scholar]
  7. Bulgin, C. E. , Palmer, P. I. , Thomas, G. E. , Arnold, C. P. , Campmany, E. , Carboni, E. , & Lawrence, B. N. (2008). Regional and seasonal variations of the Twomey indirect effect as observed by the ATSR‐2 satellite instrument. Geophysical Research Letters, 35, L02811 10.1029/2007GL031394 [DOI] [Google Scholar]
  8. Chen, T. , Guo, J. , Li, Z. , Zhao, C. , Liu, H. , Cribb, M. , et al. (2016). A CloudSat perspective on the cloud climatology and its association with aerosol perturbations in the vertical over eastern China. Journal of the Atmospheric Sciences, 73(9), 3599–3616. 10.1175/JAS-D-15-0309.1 [DOI] [Google Scholar]
  9. Chen, Y. C. , Christensen, M. W. , Stephens, G. L. , & Seinfeld, J. H. (2014). Satellite‐based estimate of global aerosol–cloud radiative forcing by marine warm clouds. Nature Geoscience, 7(9), 643–646. 10.1038/ngeo2214 [DOI] [Google Scholar]
  10. Christensen, M. W. , Chen, Y. C. , & Stephens, G. L. (2016). Aerosol indirect effect dictated by liquid clouds. Journal of Geophysical Research: Atmospheres, 121, 14,636–14,650. 10.1002/2016JD025245 [DOI] [Google Scholar]
  11. Costantino, L. , & Bréon, F. M. (2010). Analysis of aerosol‐cloud interaction from multi‐sensor satellite observations. Geophysical Research Letters, 37, L11801 10.1029/2009GL041828 [DOI] [Google Scholar]
  12. Costantino, L. , & Bréon, F. M. (2013). Aerosol indirect effect on warm clouds over South‐East Atlantic, from co‐located MODIS and CALIPSO observations. Atmospheric Chemistry and Physics, 12(6), 14,197–14,246. 10.5194/acpd-12-14197-2012 [DOI] [Google Scholar]
  13. Crosbie, E. , Youn, J. S. , Balch, B. , Wonaschütz, A. , Shingler, T. , Wang, Z. , et al. (2015). On the competition among aerosol number, size and composition in predicting CCN variability: A multi‐annual field study in an urbanized desert. Atmospheric Chemistry and Physics, 15(12), 6943–6958. 10.5194/acp-15-6943-2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dee, D. P. , Uppala, S. M. , Simmons, A. J. , Berrisford, P. , Poli, P. , Kobayashi, S. , et al. (2011). The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. 10.1002/qj.828 [DOI] [Google Scholar]
  15. Dusek, U. , Frank, G. P. , Hildebrandt, L. , Curtius, J. , Schneider, J. , Walter, S. , et al. (2006). Size matters more than chemistry for cloud‐nucleating ability of aerosol particles. Science, 312(5778), 1375–1378. 10.1126/science.1125261 [DOI] [PubMed] [Google Scholar]
  16. Ervens, B. , Feingold, G. , & Kreidenweis, S. M. (2005). Influence of water‐soluble organic carbon on cloud drop number concentration. Journal of Geophysical Research, 110, D18211 10.1029/2004JD005634 [DOI] [Google Scholar]
  17. Fan, J. , Yuan, T. , Comstock, J. M. , Ghan, S. , Khain, A. , Leung, L. R. , et al. (2009). Dominant role by vertical wind shear in regulating aerosol effects on deep convective clouds. Journal of Geophysical Research, 114, D22206 10.1029/2009JD012352 [DOI] [Google Scholar]
  18. Grandey, B. S. , & Stier, P. (2010). A critical look at spatial scale choices in satellite‐based aerosol indirect effect studies. Atmospheric Chemistry and Physics, 10(23), 11,459–11,470. 10.5194/acp-10-11459-2010 [DOI] [Google Scholar]
  19. Gryspeerdt, E. , Quaas, J. , & Bellouin, N. (2016). Constraining the aerosol influence on cloud fraction. Journal of Geophysical Research: Atmospheres, 121, 3566–3583. 10.1002/2015JD023744 [DOI] [Google Scholar]
  20. Gryspeerdt, E. , & Stier, P. (2012). Regime‐based analysis of aerosol‐cloud interactions. Geophysical Research Letters, 39, L21802 10.1029/2012GL053221 [DOI] [Google Scholar]
  21. Gryspeerdt, E. , Stier, P. , & Grandey, B. S. (2014). Cloud fraction mediates the aerosol optical depth‐cloud top height relationship. Geophysical Research Letters, 41, 3622–3627. 10.1002/2014GL059524 [DOI] [Google Scholar]
  22. Kaufman, Y. J. , Koren, I. , Remer, L. A. , Rosenfeld, D. , & Rudich, Y. (2005). The effect of smoke, dust, and pollution aerosol on shallow cloud development over the Atlantic Ocean. Proceedings of the National Academy of Sciences of the United States of America, 102(32), 11,207–11,212. 10.1073/pnas.0505191102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Klein, S. A. , & Hartmann, D. L. (1993). The seasonal cycle of low stratiform clouds. Journal of Climate, 6(8), 1587–1606. 10.1175/1520-0442(1993)006%3C1587:TSCOLS%3E2.0.CO;2 [DOI] [Google Scholar]
  24. Kleinman, L. I. , Daum, P. H. , Lee, Y. N. , Lewis, E. R. , Sedlacek, A. J. III , Senum, G. I. , et al. (2012). Aerosol concentration and size distribution measured below, in, and above cloud from the DOE G‐1 during VOCALS‐REx. Atmospheric Chemistry and Physics, 12(1), 207–223. 10.5194/acp-12-207-2012 [DOI] [Google Scholar]
  25. Koren, I. , Feingold, G. , & Remer, L. A. (2010). The invigoration of deep convective clouds over the Atlantic: Aerosol effect, meteorology or retrieval artifact? Atmospheric Chemistry and Physics, 10(18), 8855–8872. 10.5194/acp-10-8855-2010 [DOI] [Google Scholar]
  26. Koren, I. , Kaufman, Y. J. , Rosenfeld, D. , Remer, L. A. , & Rudich, Y. (2005). Aerosol invigoration and restructuring of Atlantic convective clouds. Geophysical Research Letters, 32, L14828 10.1029/2005GL023187 [DOI] [Google Scholar]
  27. Lance, S. , Nenes, A. , & Rissman, T. A. (2004). Chemical and dynamical effects on cloud droplet number: Implications for estimates of the aerosol indirect effect. Journal of Geophysical Research, 109, D22208 10.1029/2004JD004596 [DOI] [Google Scholar]
  28. Levy, R. C. , Mattoo, S. , Munchak, L. A. , Remer, L. A. , Sayer, A. M. , Patadia, F. , et al. (2013). The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques, 6(1), 159–259. 10.5194/amtd-6-159-2013 [DOI] [Google Scholar]
  29. Lihavainen, H. , Kerminen, V. M. , & Remer, L. A. (2010). Aerosol‐cloud interaction determined by both in situ and satellite data over a northern high‐latitude site. Atmospheric Chemistry and Physics, 10(22), 10,987–10,995. 10.5194/acp-10-10987-2010 [DOI] [Google Scholar]
  30. Liu, Y. , Leeuw, G. D. , Kerminen, V. M. , Zhang, J. , Zhou, P. , Nie, W. , et al. (2017). Analysis of aerosol effects on warm clouds over the Yangtze River Delta from multi‐sensor satellite observations. Atmospheric Chemistry and Physics, 17(9), 5623–5641. 10.5194/acp-17-5623-2017 [DOI] [Google Scholar]
  31. Loeb, N. G. , & Schuster, G. L. (2008). An observational study of the relationship between cloud, aerosol and meteorology in broken low‐level cloud conditions. Journal of Geophysical Research, 113, D14214 10.1029/2007JD009763 [DOI] [Google Scholar]
  32. Mauger, G. S. , & Norris, J. R. (2007). Meteorological bias in satellite estimates of aerosol‐cloud relationships. Geophysical Research Letters, 34, L16824 10.1029/2007GL029952 [DOI] [Google Scholar]
  33. McFiggans, G. , Artaxo, P. , Baltensperger, U. , Coe, H. , Facchini, M. C. , Feingold, G. , et al. (2006). The effect of physical and chemical aerosol properties on warm cloud droplet activation. Atmospheric Chemistry and Physics, 6(9), 2593–2649. 10.5194/acp-6-2593-2006 [DOI] [Google Scholar]
  34. Meng, J. W. , Yeung, M. C. , Li, Y. J. , Lee, B. Y. L. , & Chan, C. K. (2014). Size‐resolved cloud condensation nuclei (CCN) activity and closure analysis at the HKUST Supersite in Hong Kong. Atmospheric Chemistry and Physics, 14(18), 10,267–10,282. 10.5194/acp-14-10267-2014 [DOI] [Google Scholar]
  35. Nakajima, T. , Higurashi, A. , Kawamoto, K. , & Penner, J. E. (2001). A possible correlation between satellite‐derived cloud and aerosol microphysical parameters. Geophysical Research Letters, 28(7), 1171–1174. 10.1029/2000GL012186 [DOI] [Google Scholar]
  36. Nenes, A. , Charlson, R. J. , Facchini, M. C. , Kulmala, M. , Laaksonen, A. , & Seinfeld, J. H. (2002). Can chemical effects on cloud droplet number rival the first indirect effect? Geophysical Research Letters, 29(17), 1848 10.1029/2002GL015295 [DOI] [Google Scholar]
  37. Padró, L. T. , Moore, R. H. , Zhang, X. , Rastogi, N. , Weber, R. J. , & Nenes, A. (2012). Mixing state and compositional effects on CCN activity and droplet growth kinetics of size‐resolved CCN in an urban environment. Atmospheric Chemistry and Physics, 12(21), 10,239–10,255. 10.5194/acp-12-10239-2012 [DOI] [Google Scholar]
  38. Paramonov, M. , Aalto, P. P. , Asmi, A. , Prisle, N. , Kerminen, V. M. , Kulmala, M. , & Petäjä, T. (2013). The analysis of size‐segregated cloud condensation nuclei counter (CCNC) data and its implications for cloud droplet activation. Atmospheric Chemistry and Physics, 13(20), 10,285–10,301. 10.5194/acp-13-10285-2013 [DOI] [Google Scholar]
  39. Pawlowska, H. , & Brenguier, J. L. (2000). Microphysical properties of stratocumulus clouds during ACE‐2. Tellus B, 52(2), 868–887. 10.1034/j.1600-0889.2000.00076.x [DOI] [Google Scholar]
  40. Penner, J. E. , Xu, L. , & Wang, M. (2011). Satellite methods underestimate indirect climate forcing by aerosols. Proceedings of the National Academy of Sciences, 108(33), 13,404–13,408. 10.1073/pnas.1018526108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Petters, M. D. , & Kreidenweis, S. M. (2007). A single parameter representation of hygroscopic growth and cloud condensation nucleus activity. Atmospheric Chemistry and Physics, 7(8), 1961–1971. 10.5194/acp-7-1961-2007 [DOI] [Google Scholar]
  42. Platnick, S. , Meyer, K. G. , King, M. D. , Wind, G. , Amarasinghe, N. , Marchant, B. , et al. (2017). The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Transactions on Geoscience and Remote Sensing, 55(1), 502–525. 10.1109/TGRS.2016.2610522 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Quaas, J. , Boucher, O. , Bellouin, N. , & Kinne, S. (2008). Satellite‐based estimate of the direct and indirect aerosol climate forcing. Journal of Geophysical Research, 113, D05204 10.1029/2007JD008962 [DOI] [Google Scholar]
  44. Quaas, J. , Stevens, B. , Stier, P. , & Lohmann, U. (2010). Interpreting the cloud cover–aerosol optical depth relationship found in satellite data using a general circulation model. Atmospheric Chemistry and Physics, 10(13), 6129–6135. 10.5194/acp-10-6129-2010 [DOI] [Google Scholar]
  45. Roberts, G. C. , Ramana, M. V. , Corrigan, C. , Kim, D. , & Ramanathan, V. (2008). Simultaneous observations of aerosol–cloud–albedo interactions with three stacked unmanned aerial vehicles. Proceedings of the National Academy of Sciences, 105(21), 7370–7375. 10.1073/pnas.0710308105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Saponaro, G. , Kolmonen, P. , Sogacheva, L. , Rodriguez, E. , Virtanen, T. , & Leeuw, G. D. (2017). Estimates of the aerosol indirect effect over the Baltic Sea region derived from 12 years of MODIS observations. Atmospheric Chemistry and Physics, 17(4), 3133–3143. 10.5194/acp-17-3133-2017 [DOI] [Google Scholar]
  47. Sayer, A. M. , Hsu, N. C. , Bettenhausen, C. , & Jeong, M.‐J. (2013). Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data. Journal of Geophysical Research: Atmospheres, 118, 7864–7872. 10.1002/jgrd.50600 [DOI] [Google Scholar]
  48. Sekiguchi, M. , Nakajima, T. , Suzuki, K. , Kawamoto, K. , Higurashi, A. , Rosenfeld, D. , et al. (2003). A study of the direct and indirect effects of aerosols using global satellite data sets of aerosol and cloud parameters. Journal of Geophysical Research, 108(D22), 4699 10.1029/2002JD003359 [DOI] [Google Scholar]
  49. Stier, P. (2016). Limitations of passive remote sensing to constrain global cloud condensation nuclei. Atmospheric Chemistry and Physics, 16(10), 6595–6607. 10.5194/acp-16-6595-2016 [DOI] [Google Scholar]
  50. Su, W. , Loeb, N. G. , Xu, K. M. , Schuster, G. L. , & Eitzen, Z. A. (2010). An estimate of aerosol indirect effect from satellite measurements with concurrent meteorological analysis. Journal of Geophysical Research, 115, D18219 10.1029/2010JD013948 [DOI] [Google Scholar]
  51. Tang, J. , Wang, P. , Mickley, L. J. , Xia, X. , Liao, H. , Yue, X. , et al. (2014). Positive relationship between liquid cloud droplet effective radius and aerosol optical depth over Eastern China from satellite data. Atmospheric Environment, 84, 244–253. 10.1016/j.atmosenv.2013.08.024 [DOI] [Google Scholar]
  52. Topping, D. O. , & McFiggans, G. (2012). Tight coupling of particle size, number and composition in atmospheric cloud droplet activation. Atmospheric Chemistry and Physics, 12(7), 3253–3260. 10.5194/acp-12-3253-2012 [DOI] [Google Scholar]
  53. Twomey, S. (1977). The influence of pollution on the shortwave albedo of clouds. Journal of the Atmospheric Sciences, 34(7), 1149–1152. 10.1175/1520-0469(1977) [DOI] [Google Scholar]
  54. Wang, F. , Guo, J. , Wu, Y. , Zhang, X. , Deng, M. , Li, X. , et al. (2014). Satellite observed aerosol‐induced variability in warm cloud properties under different meteorological conditions over eastern China. Atmospheric Environment, 84, 122–132. 10.1016/j.atmosenv.2013.11.018 [DOI] [Google Scholar]
  55. Wang, F. , Guo, J. , Zhang, J. , Huang, J. , Min, M. , Chen, T. , et al. (2015). Multi‐sensor quantification of aerosol‐induced variability in warm clouds over eastern China. Atmospheric Environment, 113, 1–9. 10.1016/j.atmosenv.2015.04.063 [DOI] [Google Scholar]
  56. Wang, J. , Lee, Y. N. , Daum, P. H. , Jayne, J. , & Alexander, M. L. (2008). Effects of aerosol organics on cloud condensation nucleus (CCN) concentration and first indirect aerosol effect. Atmospheric Chemistry and Physics, 8(3), 9783–9818. 10.5194/acpd-8-9783-2008 [DOI] [Google Scholar]
  57. Werner, F. , Ditas, F. , Siebert, H. , Simmel, M. , Wehner, B. , Pilewskie, P. , et al. (2014). Twomey effect observed from collocated microphysical and remote sensing measurements over shallow cumulus. Journal of Geophysical Research: Atmospheres, 119, 1534–1545. 10.1002/2013JD020131 [DOI] [Google Scholar]
  58. Wilcox, E. M. , Roberts, G. , & Ramanathan, V. (2006). Influence of aerosols on the shortwave cloud radiative forcing from North Pacific oceanic clouds: Results from the Cloud Indirect Forcing Experiment (CIFEX). Geophysical Research Letters, 33, L21804 10.1029/2006GL027150 [DOI] [Google Scholar]
  59. Yuan, T. , Li, Z. , Zhang, R. , & Fan, J. (2008). Increase of cloud droplet size with aerosol optical depth: An observation and modeling study. Journal of Geophysical Research, 113, D04201 10.1029/2007JD008632 [DOI] [Google Scholar]
  60. Zhang, F. , Li, Z. , Li, Y. , Sun, Y. , Wang, Z. , Li, P. , et al. (2016). Impacts of organic aerosols and its oxidation level on CCN activity from measurement at a suburban site in China. Atmospheric Chemistry and Physics, 16(8), 5413–5425. 10.5194/acp-16-5413-2016 [DOI] [Google Scholar]
  61. Zhang, Q. , Meng, J. , Quan, J. , Gao, Y. , Zhao, D. , Chen, P. , & He, H. (2012). Impact of aerosol composition on cloud condensation nuclei activity. Atmospheric Chemistry and Physics, 12(8), 3783–3790. 10.5194/acp-12-3783-2012 [DOI] [Google Scholar]
  62. Zhang, Q. , Quan, J. , Tie, X. , Huang, M. , & Ma, X. (2011). Impact of aerosol particles on cloud formation: Aircraft measurements in China. Atmospheric Environment, 45(3), 665–672. 10.1016/j.atmosenv.2010.10.025 [DOI] [Google Scholar]
  63. Zhang, Z. , Ackerman, A. S. , Feingold, G. , Platnick, S. , Pincus, R. , & Xue, H. (2012). Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large‐eddy simulations. Journal of Geophysical Research, 117, D19208 10.1029/2012JD017655 [DOI] [Google Scholar]
  64. Zhang, Z. , & Platnick, S. (2011). An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands. Journal of Geophysical Research, 116, D20215 10.1029/2011JD016216 [DOI] [Google Scholar]
  65. Zhu, Y. , Rosenfeld, D. , Yu, X. , & Li, Z. (2015). Separating aerosol microphysical effects and satellite measurement artifacts of the relationships between warm rain onset height and aerosol optical depth. Journal of Geophysical Research: Atmospheres, 120, 7726–7736. 10.1002/2015JD023547 [DOI] [Google Scholar]

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