• ADVANCES IN ATMOSPHERIC SCIENCES, 2018, 35(2): 169-181
doi: 10.1007/s00376-017-7106-2
First Surface-based Estimation of the Aerosol Indirect Effect over a Site in Southeastern China
Jianjun LIU1,, Zhanqing LI1,2

Abstract:

The deployment of the U.S. Atmospheric Radiation Measurement mobile facility in Shouxian from May to December 2008 amassed the most comprehensive set of measurements of atmospheric, surface, aerosol, and cloud variables in China. This deployment provided a unique opportunity to investigate the aerosol-cloud interactions, which are most challenging and, to date, have not been examined to any great degree in China. The relationship between cloud droplet effective radius (CER) and aerosol index (AI) is very weak in summer because the cloud droplet growth is least affected by the competition for water vapor. Mean cloud liquid water path (LWP) and cloud optical depth (COD) significantly increase with increasing AI in fall. The sensitivities of CER and LWP to aerosol loading increases are not significantly different under different air mass conditions. There is a significant correlation between the changes in hourly mean AI and the changes in hourly mean CER, LWP, and COD. The aerosol first indirect effect (FIE) is estimated in terms of relative changes in both CER (FIE CER) and COD (FIE COD) with changes in AI for different seasons and air masses. FIE COD and FIE CER are similar in magnitude and close to the typical FIE value of ∼0.23, and do not change much between summer and fall or between the two different air mass conditions. Similar analyses were done using spaceborne Moderate Resolution Imaging Spectroradiometer data. The satellite-derived FIE is contrary to the FIE estimated from surface retrievals and may have large uncertainties due to some inherent limitations.

Key words: ground-based measurements; aerosol indirect effect; southeastern China;
摘要: 2008年5月至12月, 美国大气辐射观测移动设施在我国寿县地区进行了连续观测, 并获得了该地区大气, 地面, 气溶胶以及云等大量的综合观测资料. 该观测为地基研究我国气溶胶和云的相互作用提供了极为难得的机会. 气溶胶与云的相互作用是气候变化研究中最具有挑战性的科学问题之一, 而到目前为止, 我国几乎没有地基观测研究. 由于云滴粒子增长受到水汽竞争的影响, 云滴有效半径与气溶胶指数之间的关系在夏季较弱. 秋季, 云平均的液态水路径以及云光学厚度随气溶胶指数的增加显著增加. 云滴有效半径和云液态水路径对气溶胶增加的敏感性在不同大气团影响下并没有显著的差异. 平均的气溶胶指数的小时变化与平均的云滴有效半径, 液态水路径和光学厚度存在显著的相关性. 利用云滴有效半径和云光学厚度对气溶胶指数的相对变化, 分别评估了不同季节以及不同气团影响下的气溶胶第一间接效应的量级. 利用两个云的参数计算的气溶胶第一间接效应的量级相似, 接近于典型的第一间接效应的量级(~0.23), 并且在夏季与冬季以及不同的两个气团情况下并不存在明显的变化. 与地面观测值得到的第一间接效应相比, 由于某些固有的限制, 利用中分辨率成像光谱仪(MODIS)得到的第一间接效应存在着很大的不缺定性.
关键词: 地基观测 ; 气溶胶间接效应 ; 中国东南部
1. Introduction

Atmospheric aerosol particles can directly affect Earth's radiative balance by absorbing and scattering solar radiation (direct effects). They can also indirectly alter cloud microphysical and macrophysical properties, and precipitation, by serving as cloud condensation nuclei (CCN) (indirect effects). For a fixed cloud liquid water path (LWP), the cloud droplet size decreases with increasing CCN and reflects more energy to space. This is called the aerosol first indirect effect (FIE) or Twomey effect (Twomey, 1977). Under overcast sky conditions in the midlatitudes, the radiative forcing induced by the aerosol indirect effect varies from -3 to -10 W m-2 for each 0.05 increment in FIE (McComiskey and Feingold, 2008). Although the FIE has been studied extensively, it remains one of the largest uncertainties of all known climate forcing mechanisms (IPCC, 2013).

Using satellite measurements, the FIE has been investigated on regional (Nakajima et al., 2001; Liu et al., 2003; Menon et al., 2008; Yuan et al., 2008) and global (Bréon et al., 2002) scales. Such studies suffer from major inherent retrieval problems associated with retrievals of aerosol loading in general (Li et al., 2009), and aerosols near cloud edges in particular (Várnai and Marshak, 2014), which originate from the fundamental limitation that aerosol and cloud properties cannot be retrieved at the same time over the same location. These limitations can be overcome or lessened by ground and in-situ measurements (Feingold et al., 2003, 2006; Kim et al., 2003; Garrett et al., 2004; Pandithurai et al., 2009; Ma et al., 2010). (McComiskey and Feingold, 2012) argued that aerosol-cloud interactions (ACI) can only be assessed accurately from aircraft or ground-based in-situ data and depend on cloud and meteorological regimes (Wang et al., 2012; Zhang et al., 2016). Long-term observations of atmospheric variables and aerosol and cloud properties are thus needed to study the sensitivity of cloud properties to aerosols in different climatic regions.

East Asia, especially southeastern China, is a fast developing and densely populated region where anthropogenic emissions of aerosol particles and precursors are high and the aerosol composition is complex (Li et al., 2007; Lee et al., 2010; Liu et al., 2011a, 2012). Aerosol optical properties over East Asia and their influence on the radiation budget at the surface (Xu et al., 2002; Xia et al., 2007; Liu et al., 2012) and within the atmosphere (Liu et al., 2012) have been investigated extensively. However, up until now, no FIE study has been done using ground-based measurements made in China due to the dearth of coincident aerosol, cloud, and meteorological observations (Liu et al., 2013, 2015). Only a few FIE studies using satellite measurements have been carried out (Yuan et al., 2008; Tang et al., 2014). Significant anti-Twomey effects have been reported based upon Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals made over southeastern China (Yuan et al., 2008; Wang et al., 2014).

Two major international field experiments have been conducted in mainland China with the goal of studying aerosol properties and their direct and indirect effects: the "East Asian Study of Tropospheric Aerosols: An International Regional Experiment" (Li et al., 2007) and the "East Asian Study of Tropospheric Aerosols and Impact on Regional Climate" (Li et al., 2011). In the latter experiment, the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF-China) was deployed at Shouxian (SX), approximately 500 km northwest of Shanghai City, from May to December 2008. The experiment provided simultaneous observations of clouds, aerosols, radiative properties, and meteorological factors for the first time, and the opportunity to study the influence of aerosols on radiative fluxes, clouds, and precipitation in this key region.

Taking advantage of the extensive measurements made during AMF-China, the first comprehensive investigation into ACI over this polluted region is presented based upon the dataset compiled at SX. Brief descriptions of the measurements and methods used in the analyses are given in section 2. Section 3 presents the aerosol and cloud properties and analyses of the influence of aerosols on cloud properties for different seasons and air mass conditions. A summary is given in section 4.

2. Measurements and methodology
2.1. Cloud optical and microphysical properties

The cloud optical depth (COD) was retrieved following the method described by (Marshak et al., 2004) and (Chiu et al., 2010). The method is based on ground-based measurements of zenith radiances at red and near-infrared channels where the surface albedos in the two channels differ significantly. The downwelling zenith radiance was measured by a two-channel narrow field-of-view (NFOV) radiometer with a 5.7° field of view at 673 and 870 nm and at a 1-s time resolution. Biases in the NFOV radiance measurements were quantified through comparisons with Aerosol Robotic Network sun photometer radiance measurements, which are deemed to be more accurate (Holben et al., 1998). In general, the two datasets correlate well at both wavelengths. However, NFOV-measured zenith radiances at 673 nm are underestimated by ∼ 11%. On the basis of regression analyses, adjusted NFOV zenith radiances at 673 nm, F673, adj, were calculated using the following formula: $$F_{673,{\rm adj}}=1.1066F_{673,{\rm obs}}-0.0007 ,\ \ (1)$$ where F673, obs represents the originally-measured zenith radiances. The total uncertainty in COD retrievals using the zenith radiance measurement method is ∼ 17% (Chiu et al., 2010).

Atmospheric brightness temperatures at 12 frequencies were measured by a profiling microwave radiometer (TP/ WVP-3000, Radiometrics Corporation, US) and installed at SX. Vertical profiles of temperature and humidity, as well as integrated water vapor column amount and LWP, were retrieved with a 1-min time resolution using this instrument. Retrieval coefficients were derived for each season to account for seasonal variations in the atmosphere and in the mean radiating temperature. Typical uncertainties in LWP retrievals from microwave radiometers are ∼ 20 g m-2 for LWP <200 g m-2 and ∼ 10% for LWP >200 g m-2 (Liljegren et al., 2001; Liljegren and Lesht, 2004; Dong et al., 2008). When COD and LWP are retrieved, the cloud droplet effective radius (CER) can be calculated using the following equation: $$\label{eq1} \tau=\frac{3{\rm LWP}}{2\rho_{\rm w}r_{\rm e}} , \ \ (2)$$ where τ represents COD, r e represents CER, and ρ w is the density of water. The uncertainty in CER is determined by the uncertainties in COD and LWP. Cloud retrievals following the same method have been used to assess ACI in marine stratus clouds (McComiskey et al., 2009). Cloud-base heights were measured by a micropulse lidar (MPL), which is an elastic backscatter lidar developed by the NASA Goddard Space Corporation (Wang and Sassen, 2001). The MPL installed at SX operates at 527 nm and has a 30-m range vertical resolution and a 30-s time resolution.

In this study, clouds with base heights greater than 3 km and CER >25 μm or CER <2 μm are excluded because these are unrealistic values for low-level warm clouds (Bulgin et al., 2008). Also, clouds with LWP <20 g m-2 and LWP >700 g m-2 are excluded to remove unaccounted-for biases and rainy periods, respectively (Min et al., 2003; Liu et al., 2013). To examine the influence of aerosols on non-precipitating cloud properties, only clouds with LWP <300 g m-2 are analyzed (Chiu et al., 2012; Harikishan et al., 2016).

Table 1. Means and standard deviations of aerosol, cloud, and surface meteorological variables in summer (JJA), fall (SON), and December (Dec.) and for each air mass type.

2.2. Aerosol properties

Total scattering coefficients (σ) of aerosol particles with diameters <1 μm and <10 μm were measured by two three-wavelength (450, 550, and 700 nm) nephelometers (Model 3653, TSI, US) with a 1-min time resolution under dry conditions with relative humidity (RH) less than 40% and varying RH levels. The nephelometers were calibrated weekly using CO2 gas to ensure the accuracy of measurements. The uncertainty in nephelometer-measured σ ranges from 1-4 Mm-1 for 1-min averages (Heintzenberg et al., 2006). Here, the σ of particles with diameters <10 μm measured at 450 and 550 nm under dry conditions were used to calculate the Ångström exponent (AE). The aerosol index (AI) was then calculated using the σ at 550 nm and the AE. The AI was used as a proxy for CCN to study ACI because AI is more related to CCN concentration than σ (Nakajima et al., 2001; Liu and Li, 2014). The aerosol properties were then matched with cloud retrievals and integrated over 1-min time intervals.

2.3. Air mass trajectory classification

The prevalent meteorological conditions and dominant aerosol type largely depend on the air mass. Potential differences in aerosols, cloud properties, and ACI for different air masses at SX were accounted for using a Hybrid Single-Particle Lagrangian Integrated Trajectory model simulation (Stein et al., 2015; Rolph, 2016). All three-day simulated air mass back trajectories arriving at the site at 500 m were classified into four major clusters: air masses originating from the northwest (cluster I), the northeast (cluster II), the East China Sea (cluster III), and the south (cluster IV) of China. Detailed descriptions of each air mass are discussed in the study by (Liu et al., 2011b). The number of days with a cluster I, II, III, and IV air mass is 30, 46, 81, and 37, respectively. This corresponds to 15.5%, 23.7%, 41.8%, and 19%, respectively, of the total number of days of the field campaign at SX.

3. Results
3.1. Characteristics of aerosol and cloud properties during the observation period

3.1.1. Seasonal characteristics of aerosol and cloud properties

Mean aerosol and cloud properties in summer (June, July, and August; JJA), fall (September, October, and November; SON), and winter (December; Dec.) are summarized in Table 1. The probability distribution functions (PDFs) of aerosol properties (σ, AE, and AI) and cloud properties (COD, LWP, and CER) for JJA, SON, and Dec. are shown in Fig. 1. The number of aerosol and cloud samples in each season are shown in panels (c) and (f), respectively, of the figure. Aerosol loadings in JJA and Dec. are similar and smaller than that in fall. The small magnitude of mean σ in JJA occurs because there is more frequent precipitation and relatively higher mixing heights in that season. Relatively strong winds play a major role in the low mean σ in Dec. (Fan et al., 2010). Mean values of AE in JJA, SON, and Dec. are similar, suggesting that aerosols have similar mean aerosol particle sizes during the three periods. The broad distribution of AE (Fig. 1b) indicates that aerosol particle sizes in all seasons are highly variable.

The COD varies greatly, with larger mean values in Dec. than in JJA and SON (Fig. 1d). Distributions of COD in JJA and SON have similar shapes and significantly more cloud samples with large COD are observed in Dec. Clouds have the largest LWP values in SON and similar values in JJA and Dec. (Fig. 1e). During all three periods, clouds have similar distributions of LWP, with more samples in the range of 60-120 g m-2. The CER is smallest in Dec., with most of the distribution located below 5 μm (Fig. 1f).

Fig.1. PDFs of aerosol and cloud properties in JJA (blue lines), SON (green lines) and December (red lines): (a) scattering coefficient (σ); (b) Ångström exponent (AE), (c) aerosol index (AI); (d) cloud optical depth (COD), (e) liquid water path (LWP); and (f) cloud droplet effective radius (CER). The number of aerosol and cloud samples in each season are shown in panels (c) and (f), respectively.

Fig.2. As in Fig. 1 but for different air masses.

3.1.2. Characteristics of aerosol and cloud properties under different air mass conditions

Table 1 also lists the means and standard deviations of aerosol and cloud properties for each air mass type. Air masses I and IV have the highest and smallest mean values of σ, respectively. The σ for air mass IV has a narrower distribution than that for other air masses (Fig. 2a). Figure 2b shows that relatively more aerosol particles with large particle sizes arrive at the site from the northwest and northeast, and relatively more aerosol particles with small particle sizes arrive at the site from the east and southeast. The AIs for the different air masses have similar properties to the σ.

Mean values of COD and LWP are largest (smallest) when air mass I (IV) influences the site, and the mean CER is largest (smallest) under air mass IV (I) conditions. Figure 2e shows that most of the LWP values are less than 200 g m-2 for all air masses. The PDFs of CER for each air mass in Fig. 2f have similar shapes. A shift in CER toward larger values from air mass I to air mass IV is apparent.

Fig.3. Diurnal cycle of the (a) σ, (b) AE, (c) AI, (d) COD, (e) LWP, and (f) CER during the observation period.

3.1.3. Diurnal cycle of aerosol and cloud properties

Hourly mean aerosol and cloud properties are calculated from all available samples in each hour from 0800 to 1600 local standard time (LST; Fig. 3). Hourly mean σ varies significantly, with the highest value in the morning and the lowest value in the afternoon. The high value of σ in the morning is mainly due to local cooking emissions and emissions from transportation sources, as well as the low mixing height because of the low temperature (Fan et al., 2010; Liu et al., 2011b). After sunrise, the temperature, and thus the mixing height, increases, which leads to the dilution of surface aerosols with air aloft and results in a corresponding decrease in σ (Fan et al., 2010). Figure 3b shows that AE slightly increases from morning to afternoon, but the increase is small, suggesting that the aerosol particle size does not change much from morning to afternoon. The diurnal cycle of AI in Fig. 3c is similar to the diurnal cycle of σ.

The hourly mean COD is nearly constant from morning to noon, and then significantly increases from noon to late afternoon, suggesting that clouds attenuate more in the afternoon than in the morning. In general, the variations in hourly mean LWP from morning to afternoon seen in Fig. 3e vary in the same way as the hourly mean COD, with maxima occurring around noon. CER increases slightly from morning to noon, then decreases significantly (Fig. 3f).

3.2. Influence of aerosols on cloud properties

3.2.1. Relationship between cloud properties and AI for different seasons

Figures 4a-c show the logarithmic relationship between CER, LWP, COD, and AI during the JJA and SON periods, respectively. The total number of samples in JJA and SON is 2343 and 2812, respectively. Cloud properties were sorted as a function of AI and averaged in each of the 10 AI bins. Each bin contains the same number of samples. Major meteorological parameters (e.g., RH), as well as large-scale dynamic (e.g., vertical velocity) and thermodynamic (e.g., lower-tropospheric stability) parameters, show no significant seasonal differences when going from low AI to high AI. For both JJA and SON, CER decreases as AI increases, but the relationship between CER and AI is very weak for JJA. As AI increases from the lowest quartile to the highest quartile, CER decreases by ∼9% (from 10.3 μm to 9.4 μm) in JJA and by ∼ 36% (from 7.8 μm to 5.0 μm) in SON. The strength of the aerosol impact on CER is quantified by the linear regression slope of the CER-AI relationship in log-log scale (Costantino and Bréon, 2013). The powers of the calculated exponential regression functions are equal to -0.04 and -0.17 in JJA and SON, respectively. Based on MODIS measurements, (Yuan et al., 2008) showed that the slope of the correlation between CER and aerosol optical depth (AOD) is driven primarily by the water vapor amount, which explains 70% of the variance. The slope of the correlation between CER and AOD is positive for moist regions and negative for dry regions (Yuan et al., 2008). The higher water vapor amount in JJA than in SON may thus partly explain the weaker CER sensitivity to aerosol loading in JJA, in addition to other potential factors (e.g., dynamic and thermodynamic conditions, and aerosol type).

Fig.4. The (a) CER, (b) LWP and (c) COD as a function of AI in summer (gray) and fall (black). Cloud properties were first sorted as a function of AI and averaged in each of the 10 AI bins. Error bars represent the confidence level of the mean values if independent data are assumed, and are calculated as $\rm sd^2/\sqrt{N-2}$, where sd2 is the standard deviation of the cloud property data in a bin and N is the number of data points in the AI bin. Axes are scaled logarithmically.

Figure 4b shows that, in JJA, LWP does not strongly depend on AI. As AI increases from the lowest quartile to the highest quartile, the mean value of LWP changes from 136 g m-2 to 130 g m-2, which is a decrease of ∼ 4%. The linear regression slope of the LWP-AI relationship in log-log scale is -0.02. During the SON period, the mean LWP increases by ∼ 10% (from 126 g m-2 to 139 g m-2) as AI changes from the lowest quartile to the highest quartile. The linear regression slope in log-log scale is 0.06. Results from current studies on the response of LWP to increases in aerosol loading are diverse, showing a positive correlation in some studies (Quaas et al., 2009; Wang et al., 2013) and a negative correlation in others (Twohy et al., 2005; Lee et al., 2009). The balance between two competitive processes determines the response of LWP to increases in aerosol loading: (1) moistening from precipitation suppression; and (2) drying from the increased entrainment of dry overlaying air. Since the two processes commonly occur together, the difference in the response of LWP to increases in AI between the two seasons possibly happens because the different meteorology, aerosols, and other settings in each season may make one process dominate over the other.

During the JJA period, COD increases with increasing AI, but not in a significant way. As AI changes from the lowest quartile to the highest quartile, the mean COD increases by ∼7% (from 24.9 to 26.6). The weak changes in LWP and CER with increasing AI lead to a weak response of COD to AI. Figure 4c shows that, during the SON period, COD significantly increases with increasing AI. The mean value of COD increases by ∼ 63% (from 26.3 to 43.0) as AI increases from the lowest quartile to the highest quartile. The slope of the COD-AI exponential regression line is 0.21. This large value suggests that aerosols favor the growth of clouds in terms of thickness by inhibiting CER (Twomey, 1977; Liu et al., 2016), and thus enhance the reflection of solar radiation by clouds.

Fig.5. As in Fig. 4 but for air mass II (gray) and air mass III (black).

3.2.2. Relationship between cloud properties and AI under different air mass conditions

Figures 5a-c show the logarithmic relationships between CER, LWP, COD and AI under different air mass conditions. Due to the limited number of aerosol and cloud samples for air masses I and IV, only samples under air mass II and III conditions are analyzed. Major meteorological parameters under both air mass conditions show no significant difference as AI increases. Under both air mass conditions, CER significantly decreases as AI increases, suggesting a strong influence of aerosols on cloud microphysical properties. The mean CER decreases by ∼ 34% (from 5.8 to 3.8 μm) and by ∼ 22% (from 8.1 to 6.3 μm) under air mass II and III conditions, respectively. No significant difference in the strength of the sensitivity of CER to increasing aerosols is seen between both air mass types.

Figure 5b shows that, under both air mass II and III conditions, LWP increases with increasing AI, but not in a significant way. As AI increases from the lowest quartile to the highest quartile, the mean LWP increases by 20% (from 94 to 113 g m-2) and by 12% (from 135 to 151 g m-2) under air mass II and III conditions, respectively. The strength of the LWP sensitivity to increasing aerosols is almost the same under air mass II and III conditions.

Figure 5c shows that, as AI increases, the mean COD increases by ∼26% (from 34 to 43) and by ∼ 25% (from 32 to 40) when air mass II and III, respectively, influence the site. The slope of the COD-AI exponential regression line is larger for samples influenced by air mass II (0.15) than for those influenced by air mass III (0.08), suggesting that under air mass II conditions, the sensitivity of COD to aerosols is greater.

3.3. Potential role played by aerosols in the evolution of cloud properties

Figure 6 shows the PDFs of percentage changes in hourly mean CER (CER), LWP (LWP), and COD (COD) for the lowest (AI L) and highest (AI H) quartile of percentage changes in AI. The percentage change in hourly mean cloud and aerosol properties is calculated as: $$\Delta M=(\overline{M}_i-\overline{M}_{i-1})/\overline{M}_{i-1},i=9,\ldots,15 \ \ (3)$$ where M represents an aerosol or cloud variable, ∆ M is the percentage change in the variable, $\overline{M}$ is the mean value over an hour of the variable, and i is the LST. When i=9, $\overline{M}_i-1$ represents the mean value of the variable from 0800 LST to 0900 LST, and $\overline{M}_i$ represents the mean value of the variable from 0900 LST to 1000 LST. Due to the relatively short time scale (one hour), such a procedure can be viewed as a high temporal filter, since it largely removes the effects of synoptic and large-scale processes. The mean values of AI L and AI H are -0.26 and 0.22, respectively, which represents a decrease by 26% and an increase by 22%, respectively. The PDF of CER in Fig. 6a shows that there is a shift in CER toward positive values for the AI L case and toward negative values for the AI H case. For the AI L case, the mean CER is 0.18 (an increase of 18%), and for the AI H case the mean CER is -0.06 (a decrease of 6%). A negative relationship between CER and AI is found, which suggests that when AI increases after an hour, the CER tends to decrease simultaneously. Figures 6b and c show that LWP and COD are positively correlated with AI. For the AI L case, there are more samples with LWP <0 and COD <0 than with LWP >0 and COD >0, but for the AI H case the opposite is seen. This suggests that, when AI increases after an hour, the LWP and COD tend to increase simultaneously. In general, the larger the AI, the smaller the CER and the larger the LWP and COD.

Fig.6. PDFs of percentage changes in the hourly means of (a) CER (CER), (b) LWP (LWP), and (c) COD (COD) for the lowest (AI L, in gray) and highest (AI H, in black) quartile of changes in AI.

3.4. Aerosol FIE

The aerosol FIE can be calculated as $${\rm FIE}=\left.\dfrac{\partial\ln{\rm COD}}{\partial\ln\alpha}\right|_{\rm LWP}= -\left.\dfrac{\partial\ln{\rm CER}}{\partial\ln\alpha}\right|_{\rm LWP} , \ \ (4)$$ where α is the CCN concentration or a CCN proxy, such as aerosol number concentration, AOD, etc. In the current study, aerosol and cloud measurements were first separated into different LWP bins ranging from 20 to 300 g m-2 in 20 g m-2 increments. Then, the FIE in each LWP bin was estimated using the linear regression slope of all scatter points representing the CER/COD and AI relationship in log-log scale. The FIE estimated from changes in CER and COD with changes in AI is expressed as FIE CER and FIE COD, respectively.

Fig.7. Magnitudes of the first indirect effect (FIE) (bars) estimated from changes in CER with changes in AI (FIE CER) in each LWP bin from 20 to 300 g m-2 in 20 g m-2 intervals in (a) JJA and (b) SON, and (c) under air mass II and (d) air mass III conditions. The dotted lines indicate the number of samples in each LWP bin (right-hand ordinates). Only cases with sample numbers greater than 50 and with a calculated FIE CER that is statistically significant at the 95% confidence level are considered.

Fig.8. As in Fig. 7 but for the magnitude of the FIE estimated from changes in COD with changes in AI (FIE COD).

Table 2. FIE estimates based on ground-based measurements from different studies.

Figure 7 shows the magnitude of FIE CER in each LWP bin for the different seasons (Figs. 7a and b for JJA and SON, respectively) and for different air mass conditions (Figs. 7c and d for air mass II and air mass III, respectively). Only cases with sample numbers greater than 50 and with a calculated FIE CER that is statistically significant at the 95% confidence level are considered. The mean value of FIE CER is 0.16 0.06 and 0.17 0.06 in JJA and SON, respectively, which suggests that FIE CER has no systematically strong seasonal variation at the SX site. The mean FIE CER values under air mass II and III conditions (0.23 0.09 and 0.20 0.06, respectively) are not significantly different. The FIE CER in JJA and when air mass II is over the site increases as LWP increases. In SON and when air mass III is over the site, the FIE CER increases and then slightly decreases as LWP increases. Figure 8 shows the magnitude of FIE COD for each season (Fig. 8a for JJA and Fig. 8b for SON) and for each air mass (Fig. 8c for air mass II and Fig. 8d for air mass III). The FIE COD is similar to the FIE CER in each season and under each air mass condition. The variation in FIE COD with increasing LWP is also consistent with that of FIE CER in each season and under each air mass condition.

It is difficult to directly compare various estimates of FIE from different studies because the conditions under which FIE is calculated, e.g., LWP ranges and the CCN proxy used, and the method used to retrieve CER, to which the FIE might be sensitive (Rosenfeld and Feingold, 2003; Feingold et al., 2006; McComiskey et al., 2009; Zhao et al., 2012), are usually different. Most studies have shown that the magnitude of the FIE generally lies between 0.02 and 0.33, with most values between 0.05 and 0.25 (Zhao et al., 2012). Ground-based studies that focus on FIE and its quantification over East Asia, especially over China, are few due to the lack of simultaneous observations of aerosol and cloud properties, with the latter being the primary constraint. The FIE metric estimated from satellite measurements ranges from 0.02 to 0.20 for midlatitude continental clouds (Nakajima et al., 2001; Myhre et al., 2007) and is usually lower than that estimated in airborne- and surface-based studies. Table 2 summarizes results from previous studies on estimating FIE for non-precipitating, warm clouds based on ground measurements. The FIE metric calculated in this study generally falls within the range of the published values listed in Table 2, and is close to the typical FIE value of ∼ 0.23 reported by (Twomey, 1977).

3.5. FIE calculated from MODIS measurements

Based on MODIS measurements of AOD and cloud microphysical properties, previous studies have demonstrated a positive relationship between cloud CER and AOD over southeastern China (Yuan et al., 2008; Tang et al., 2014). However, a significant decrease in CER with increased surface-measured aerosol loading is found in this study. Ten years (2003-2012) of MODIS/Terra and MODIS/Aqua retrievals of AOD and cloud microphysical properties made over the site were used to examine the relationship between AOD and CER, and to see whether there are any discrepancies with results derived from surface measurements. Aerosol and cloud properties were averaged over a 50 km × 50 km box centered on the SX site. To ensure data quality, the following criteria were used (Yuan et al., 2008): (1) only overcast cloudy pixels flagged as "high confidence" by the retrieval algorithm were selected; (2) clouds with COD <5 were discarded to reduce the uncertainty in cloud particle size retrievals; and (3) only liquid water clouds with CER <25 μm were chosen. Figure 9 shows MODIS-retrieved CER as a function of MODIS-retrieved AOD for different LWP bins. Only samples from LWP bins with more than 50 samples were used to estimate the FIE. During the JJA period, the relationship between CER and AOD is very weak, with the slope fluctuating around zero across all LWP bins. During the SON period, a positive correlation between CER and AOD is found, especially in the LWP range of 20 to 40 g m-2. The weak and positive correlation between CER and AOD at the site is consistent with previous MODIS studies (Yuan et al., 2008; Tang et al., 2014), but contrary to the results obtained from surface-based retrievals presented in the current study. Substantially smaller droplet sizes under high CCN concentration conditions were simulated at the SX site using the Weather Research and Forecasting model (Fan et al., 2012), which is consistent with the results of this study.

Fig.9. CER as a function of AOD for different LWP bins: (a) JJA; (b) SON. Data are from MODIS on the Terra and Aqua platforms and cover the period 2003-12.

4. Summary

The ARM mobile facility was stationed at SX in southeastern China from May to December 2008 with the purpose of collecting measurements aimed at studying the influence of aerosols on radiative fluxes, clouds, and precipitation. To the authors' knowledge, this was the first time that simultaneous measurements of cloud, aerosol, and radiative properties, as well as meteorological quantities, were made in this key region. This study presents the statistics of aerosol and cloud properties in different seasons and under different air mass conditions, then examines the influences of aerosols on cloud properties based on surface measurements made at this heavily polluted site.

In both summer and fall, CER decreases as AI increases, but the relationship is weaker in summer. There is little dependence of LWP on AI in summer, but in fall LWP increases significantly with increasing AI. There is a significant increase in COD as AI increases in fall, but not in summer. When air mass II (originating from northeastern China) and III (originating from over the ocean to the east of the site) influence the site, CER (COD) significantly decreases (increases) with increasing AI.

There is a shift in percentage changes in hourly mean CER (CER) toward positive and negative values for the lowest (AI L) and highest (AI H) quartile of percentage changes in AI cases, respectively. For the AI L case, the mean CER is 0.18 (an increase of 18%), and for the AI H case the mean CER is -0.06 (a decrease of 6%). This suggests that, when the AI increases after an hour, the CER tends to decrease, and LWP and COD tend to increase simultaneously. The magnitude of the aerosol FIE with respect to both CER and COD in summer and fall and for air mass II and III was estimated based on ground-based measurements divided into different LWP bins. The mean FIE CER in summer (fall) is equal to 0.16 0.06 (0.17 0.06) and equal to 0.23 0.09 (0.20 0.06) when air mass II (III) influences the site. The magnitudes of mean FIE COD in summer and fall and for each air mass type are similar to those of FIE CER. This suggests that the FIE has no systematically strong seasonal variation and no significant difference under different air mass conditions at the SX site.

Ten years (2003-2012) of MODIS/Terra and MODIS/ Aqua AOD and cloud microphysical properties retrieved at the SX site were also used to estimate the FIE from a space-based perspective. The effect of aerosols on cloud microphysical properties retrieved from satellite measurements is weak, and even contrary to the Twomey effect, and is affected by much more noise. Possible reasons are discussed. Results from studies about the aerosol indirect effect based on satellite measurements are contrary to results from surface-based retrievals, and may have large uncertainties due to some inherent limitations.

The FIE estimated in our study may be influenced by interactions and feedbacks with aerosol properties (e.g., aerosol vertical distribution, aerosol size distribution, etc.) and meteorological dynamics (e.g., vertical velocity and vertical wind shear). These quantities are difficult to measure simultaneously and are not discussed here. More studies focused on aerosol-cloud interactions are still needed in China, which is an ideal test bed for studying aerosol indirect effects due to the abundance of anthropogenic and natural aerosol particles in the atmosphere over that region.

Acknowledgements. Data were obtained from the ARM Program sponsored by the U.S. Department of Energy, Offce of Science, Offce of Biological and Environmental Research, Climate and Environmental Sciences Division. The reanalysis data were obtained from the ECMWF model runs for ARM analysis provided by the ECMWF. M. Cribb helped edit the manuscript. The study was supported by the National Basic Research “973” Program of China (Grant No. 2013CB955804), a Natural Science Foundation of China research project (Grant No. 91544217), and the U.S. National Science Foundation (Grant No. AGS1534670).

References

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[1] Enhancement of cloud droplet number concentration by anthropogenic aerosols has previously been demonstrated by in-situ measurements, but there remains large uncertainty in the resultant enhancement of cloud optical depth and reflectivity. Detection of this effect is made difficult by the large inherent variability in cloud liquid water path (LWP); the dominant influence of LWP on optical depth and albedo masks any aerosol influences. Here we use ground-based remote sensing of cloud optical depth (0304c) by narrowband radiometry and LWP by microwave radiometry to determine the dependence of optical depth on LWP, thereby permitting examination of aerosol influence; the method is limited to complete overcast conditions with single layer clouds, as determined mainly by millimeter wave cloud radar. Measurements in north central Oklahoma on 13 different days in the year 2000 show wide variation in LWP and optical depth on any given day, but with near linear proportionality between the two quantities; variance in LWP accounts as much as 97% of the variance in optical depth on individual days and for about 63% of the variance in optical depth for the whole data set. The slope of optical depth vs. LWP is inversely proportional to the effective radius of cloud droplets (re); event-average cloud droplet effective radius ranged from 5.6 00± 0.1 to 12.3 00± 0.6 0204m (average 00± uncertainty in the mean). This effective radius is negatively correlated with aerosol light scattering coefficient at the surface as expected for the aerosol indirect effect; the weak correlation (R2 = 0.24) might be due in part to vertically decoupled structure of aerosol particle concentration and possible meteorological influence such as vertical wind shear. Cloud albedo and radiative forcing for a given LWP are highly sensitive to effective radius; for solar zenith angle 6000° and typical LWP of 100 g m0908082, as effective radius decreases from 10.2 to 5.8 0204m determined on different days, the resultant decrease in calculated net shortwave irradiance at the top of the atmosphere (Twomey forcing) is about 50 W m0908082.      URL     [Cited within:] [18] Kim B.-G., M. A. Miller, S. E. Schwartz, Y. G. Liu, and Q. L. Min, 2008: The role of adiabaticity in the aerosol first indirect effect.J. Geophys. Res.,113,D05210,https://doi.org/10.1029/2007JD008961. [1] Aerosol indirect effects are the most uncertain of the climate forcing mechanisms that have operated through the industrial period. Several studies have demonstrated modifications of cloud properties due to aerosols and corresponding changes in shortwave and longwave radiative fluxes under specific cloud conditions, but some recent studies have indicated that cloud dynamical processes such as entrainment-mixing may be the primary modulator of cloud optical properties in certain situations. For example, day-to-day variations of the cloud drop effective radius ( r e ) determined from the ground-based remote sensing at the Southern Great Plains were found to be weakly associated with the variations in aerosol loading as characterized by its light-scattering coefficient at the surface, implying that other processes were impacting the cloud radiative properties. To study these other impacts, we extend a previous study to investigate the role of changes in liquid water path (LWP) and r e in single layer stratiform clouds that are induced by entrainment-mixing processes and their effects on cloud radiative properties. We quantify the degree of entrainment-mixing in terms of the adiabaticity defined as the ratio of the observed cloud liquid water path to the corresponding adiabatic value. The cloud optical depth is, as expected, governed primarily by LWP, but that adiabaticity is the next most influential factor. In contrast, r e is found to be equally sensitive to adiabaticity and LWP. In adiabatic clouds the aerosol first indirect effect is clearly observed and related to independent measures of aerosol loading. In sub-adiabatic clouds the aerosol first indirect effect is not readily observed; this may in some circumstances be due to interference from heterogeneous mixing processes that change the droplet number density in a manner that attenuates the effect.      URL     [Cited within:] [19] Lee K. H., Z. Q. Li, M. C. Cribb, J. J. Liu, L. Wang, Y. F. Zheng, X. G. Xia, H. B. Chen, and B. Li, 2010: Aerosol optical depth measurements in eastern China and a new calibration method.J. Geophys. Res.,115,D00K11,https://doi.org/10.1029/2009JD012812. [1] We present a new calibration method to derive aerosol optical depth (AOD) from the MultiFilter Rotating Shadowband Radiometer (MFRSR) under extremely hazy atmospheric conditions during the East Asian Study of Tropospheric Aerosols: an International Regional Experiment (EAST-AIRE) and the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) deployment in China. MFRSR measurements have been made at Xianghe since September 2004 and at Taihu and Shouxian since March and May 2008, respectively. Aerosol property retrievals from CIMEL Electonique, Paris, Sun and sky radiometers located at each site show that aerosol loading is substantial and highly variable during a given year (averaged daily AOD550 = 0.80 00± 0.14). The conventional application of the Langley method to calibrate the MFRSR is not possible at these sites because there is a dearth of stable atmospheric and low-AOD conditions. To overcome this limitation of the traditional Langley plot method, highest irradiance values at a given air mass during a given period are used here. These highest values can represent the clear-sky and minimum aerosol loading conditions. A scatterplot of the AOD estimated by this method with the CIMEL Sun and sky radiometer AOD shows very good agreement: correlation coefficients are on the order of 0.980900090.99, slopes range from 0.93 to 0.97, and offsets are less than 0.02 for the three sites. AOD and 0105ngstr0109m exponents were derived from application of the method to all MFRSR data acquired at the three sites. AOD values at 500 nm are 0304500 = 0.99 00± 0.71 (500090009870 = 1.45 00± 0.59) at Xianghe, 0.87 00± 0.54 (1.14 00± 0.31) at Taihu, and 0.84 00± 0.43 (1.15 00± 0.28) at Shouxian. Anthropogenic aerosols appear to dominate in the study region with significant contributions from large dust particles and influence of hydroscopic growth.      URL     [Cited within:1] [20] Lee S. S., J. E. Penner, and S. M. Saleeby, 2009: Aerosol effects on liquid-water path of thin stratocumulus clouds.J. Geophys. Res.,114,D07204,https://doi.org/10.1029/2008JD010513. [1] Thin clouds with mean liquid water path (LWP) of 09080450 g m0908082 cover 27.5% of the globe and thus play an important role in Earth's radiation budget. Radiative fluxes at Earth's surface and top of atmosphere are very sensitive to the LWP variation when the LWP becomes smaller than 09080450 g m0908082. This indicates that aerosol effects on thin clouds can have a substantial impact on the variation of global radiative forcing if LWP changes. This study examines the aerosol indirect effect through changes in the LWP in three cases of thin warm stratocumulus clouds with LWP < 50 g m0908082. We use a cloud-system resolving model coupled with a double-moment representation of cloud microphysics. Intensified interactions among the cloud droplet number concentration, condensation, and dynamics at high aerosol play a critical role in the LWP responses to aerosol increases. Increased aerosols lead to increased CDNC, providing the increased surface area of droplets where water vapor condenses. This increases condensation, and thus condensational heating, to produce stronger updrafts, leading to an increased LWP with increased aerosols in two of the cases where precipitation reaches the surface. In a case with no surface precipitation, LWP decreases with increases in aerosols. In this case, most of precipitation evaporates just below the cloud base. With decreases in aerosols, precipitation increases and leads to increasing evaporation of precipitation, thereby increasing instability around the cloud base. This leads to increased updrafts, and thus condensation, from which increased LWP results.      URL     [Cited within:1] [21] Li, Z., Coauthors, 2009: Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: A review and perspective.Annales Geophysicae27,2755-2770,https://doi.org/10.5194/angeo-27-2755-2009. As a result of increasing attention paid to aerosols in climate studies, numerous global satellite aerosol products have been generated. Aerosol parameters and underlining physical processes are now incorporated in many general circulation models (GCMs) in order to account for their direct and indirect effects on the earth's climate, through their interactions with the energy and water cycles. There exists, however, an outstanding problem that these satellite products have substantial discrepancies, that must be lowered substantially for narrowing the range of the estimates of aerosol's climate effects. In this paper, numerous key uncertain factors in the retrieval of aerosol optical depth (AOD) are articulated for some widely used and relatively long satellite aerosol products including the AVHRR, TOMS, MODIS, MISR, and SeaWiFS. We systematically review the algorithms developed for these sensors in terms of four key elements that influence the quality of passive satellite aerosol retrieval: calibration, cloud screening, classification of aerosol types, and surface effects. To gain further insights into these uncertain factors, the NOAA AVHRR data are employed to conduct various tests, which help estimate the ranges of uncertainties incurred by each of the factors. At the end, recommendations are made to cope with these issues and to produce a consistent and unified aerosol database of high quality for both environment monitoring and climate studies.      URL     [Cited within:1] [22] Li, Z. Q., Coauthors, 2007: Preface to special section on East Asian studies of tropospheric aerosols: An international regional experiment (EAST-AIRE).J. Geophys. Res.,112,D22S00,https://doi.org/10.1029/2007JD008853. Papers published in this special section report findings from the East Asian Study of Tropospheric Aerosols: An International Regional Experiment (EAST-AIRE). They are concerned with (1) the temporal and spatial distributions of aerosol loading and precursor gases, (2) aerosol single scattering albedo (SSA), (3) aerosol direct radiative effects, (4) validation of satellite products, (5) transport mechanisms, and (6) the effects of air pollution on ecosystems. Aerosol loading is heaviest in mideastern China with a mean aerosol optical depth (AOD) of 0.5 and increasing to 0.7 around major cities that reduced daily mean surface solar radiation by 0030-40 W m, but barely changed solar reflection at the top of the atmosphere. Aerosol loading, particle size and composition vary considerably with location and season. The MODIS AOD data from Collection 5 (C5) agree much better with ground data than earlier releases, but considerable discrepancies still exist because of treatments of aerosol SSA and surface albedo. Four methods are proposed/adopted to derive the SSA by means of remote sensing and in situ observation, which varies drastically with time and space. The nationwide means of AOD, 03ngstr02m exponent, and SSA (0.5 μm) in China are 0.69 ± 0.17, 1.06 ± 0.26, and 0.89 ± 0.04, respectively. Measurements of trace gases reveal substantial uncertainties in emission inventories. An analysis of aircraft measurements revealed that dry convection is an important mechanism uplifting pollutants over northern China. Model simulations of nitrogen deposition and impact of ozone pollution on net primary productivity indicate an increasing threat of air pollution on the ecosystem.      URL     [Cited within:2] [23] Li, Z. Q., Coauthors, 2011: East Asian studies of tropospheric aerosols and their impact on regional climate (EAST-AIRC): An overview.J. Geophys. Res.,116,D00K34,https://doi.org/10.1029/2010JD015257. As the most populated region of the world, Asia is a major source of aerosols with potential large impact over vast downstream areas. Papers published in this special section describe the variety of aerosols observed in China and their effects and interactions with the regional climate as part of the East Asian Study of Tropospheric Aerosols and their Impact on Regional Climate (EAST-AIRC). The majority of the papers are based on analyses of observations made under three field projects, namely, the Atmospheric Radiation Measurements ( ARM) Mobile Facility mission in China (AMF-China), the East Asian Study of Tropospheric Aerosols: An International Regional Experiment (EAST-AIRE), and the Atmospheric Aerosols of China and their Climate Effects (AACCE). The former two are U.S.-China collaborative projects, and the latter is a part of the China's National Basic Research program ( or often referred to as "973 project"). Routine meteorological data of China are also employed in some studies. The wealth of general and specialized measurements lead to extensive and close-up investigations of the optical, physical, and chemical properties of anthropogenic, natural, and mixed aerosols; their sources, formation, and transport mechanisms; horizontal, vertical, and temporal variations; direct and indirect effects; and interactions with the East Asian monsoon system. Particular efforts are made to advance our understanding of the mixing and interaction between dust and anthropogenic pollutants during transport. Several modeling studies were carried out to simulate aerosol impact on radiation budget, temperature, precipitation, wind and atmospheric circulation, fog, etc. In addition, impacts of the Asian monsoon system on aerosol loading are also simulated.      URL     [Cited within:1] [24] Liljegren J. C., B. M. Lesht, 2004: Preliminary results with the twelve-channel microwave radiometer profiler at the North Slope of Alaska Climate Research Facility. Fourteenth ARM Science Team Meeting Proceedings, Albuquerque, New Mexico. Coincident with the Arctic winter water vapor intensive operational period (IOP), the 12-channel microwave radiometer profiler (MWRP) was permanently deployed at the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) North Slope of Alaska (NSA) site at Barrow, Alaska, in February 2004. The purpose of the permanent deployment is to augment the routine onceper-weekday URL     [Cited within:1] [25] Liljegren J. C., E. E. Clothiaux, G. G. Mace, S. Kato, and X. Q. Dong, 2001: A new retrieval for cloud liquid water path using a ground-based microwave radiometer and measurements of cloud temperature.J. Geophys. Res.,106,14 485-14 500,https://doi.org/10.1029/2000JD900817. A new method to retrieve cloud liquid water path using 23.8 and 31.4 GHz microwave radiometer brightness temperature measurements is developed. This method does not depend on climatological estimates of either the mean radiating temperature of the atmosphere Tmr or the mean cloud liquid water temperature Tcloud. Rather, Tmr is estimated from surface temperature and relative humidity measurements, while Tcloud is estimated using millimeter-wave cloud radar data, together with atmospheric temperature profiles obtained from either radiosonde or rapid update cycle (RUC) model output. Simulations demonstrate that the new retrieval method significantly reduces the biases in the liquid water path estimates that are apparent in a site-specific retrieval based on monthly stratified, local climatology. An analysis of the liquid water path estimates produced by the two retrievals over four case study days illustrates trends and retrieval performances consistent with the model simulations.      URL     [Cited within:1] [26] Liu G. S., H. F. Shao, J. A. Coakley Jr., J. A. Curry, J. A. Haggerty, and M. A. Tschudi, 2003: Retrieval of cloud droplet size from visible and microwave radiometric measurements during INDOEX: Implication to aerosols' indirect radiative effect.J. Geophys. Res.,108(D1),4006,https://doi.org/10.1029/2001JD001395. [1] The effective radius of water cloud droplets is retrieved using remotely sensed passive microwave and visible data collected by aircraft during the Indian Ocean Experiment. The purpose of this study is to assess the aerosols' effect on cloud microphysical and radiative properties. To study this effect, we investigate the relationships among effective radius, liquid water path and number concentration of cloud droplets. The effective radius retrieval uses imager observations of reflected sunlight at 0.64 m and liquid water path derived from microwave measurements. Results of an error analysis show that retrieval error is the largest for thin clouds having small visible reflectances and small liquid water path. For this reason, only pixels with visible reflectances greater than 0.2 are used in our data analysis, so that the maximum RMS error in effective radius is limited to about 4 m. The relation between liquid water path and effective radius is examined for four different latitudinal regions. Results show that for the same liquid water path, effective radii are significantly smaller in the north than in the south, in correspondence to the north-south gradient of aerosol concentration in this region. In situ aircraft observations reveal larger cloud droplet number concentrations in the north than in the south. The north-south gradient of these variables are consistent with the aerosols' effect on cloud microphysics, that is, higher aerosol concentration increases the number concentration of cloud droplets, which, in turn, reduces droplet sizes given the same liquid water path and cloud thickness. Results based on comparison between data collected from northern and southern hemispheres suggest that the increase in aerosol number concentration alters cloud droplet numbers and sizes while leaving liquid water contents approximately the same.      URL     [Cited within:1] [27] Liu J. J., Z. Q. Li, 2014: Estimation of cloud condensation nuclei concentration from aerosol optical quantities: Influential factors and uncertainties.Atmos. Chem. Phys.14(1),471-483,https://doi.org/10.5194/acp-14-471-2014. Cloud condensation nuclei (CCN) is a key variable for understanding cloud formation, but it is hard to obtain on large scales on a routine basis, whereas aerosol optical quantities are more readily available. This study presents an in-depth investigation on the relationship between CCN and aerosol optical quantities in regions of distinct aerosol types using extensive measurements collected at multiple Atmospheric Radiation Measurement (ARM) Climate Research Facility (CRF) sites around the world. The influences of relative humidity (RH), aerosol hygroscopicity (if/isubRH/sub) and single scattering albedo (SSA) on the relationship are analyzed. Better relationships are found between aerosol optical depth (AOD) and CCN at the Southern Great Plains (US), Ganges Valley (India) and Black Forest sites (Germany) than those at the Graciosa Island and Niamey (Niger) sites, where sea salt and dust aerosols dominate, respectively. In general, the correlation between AOD and CCN decreases as the wavelength of AOD measurement increases, suggesting that AOD measured at a shorter wavelength is a better proxy of CCN. The correlation is significantly improved if aerosol index (AI) is used together with AOD. The highest correlation exists between CCN and aerosol scattering coefficients (sigma;subsp/sub) and scattering AI measured in-situ. The CCN-AOD (AI) relationship deteriorates with increasing RH. If RH exceeds 75%, the relationship becomes almost invalid for using AOD as a CCN proxy, whereas a tight sigma;subsp/sub-CCN relationship exists for dry particles. Aerosol hygroscopicity has a weak impact on the sigma;subsp/sub-CCN relationship. Particles with low SSA are generally associated with higher CCN concentrations, suggesting that SSA affects the relationship between CCN concentration and aerosol optical quantities. It may thus be used as a constraint to reduce uncertainties in the relationship. A significant increase in sigma;subsp/sub and decrease in CCN with increasing SSA is observed, leading to a significant decrease in their ratio (CCN/sigma;subsp/sub) with increasing SSA. The relationships and major influential factors are parameterization for improving CCN estimation with varying amount of information on RH, particle size and SSA.      URL     [Cited within:2] [28] Liu J. J., Y. F. Zheng, Z. Q. Li, C. Flynn, E. J. Welton, and M. Cribb, 2011a: Transport,vertical structure and radiative properties of dust events in southeast China determined from ground and space sensors.Atmos. Environ.,45(35),6469-6480,https://doi.org/10.1016/j.atmosenv.2011.04. 031. Two dust events were detected over the Yangtze Delta region of China during March 14–17 and April 25–26 in 2009 where such dust events are uncommon. The transport behavior, spatio-temporal evolution, vertical structure, direct radiative effects, as well as induced heating rates, are investigated using a combination of ground-based and satellite-based measurements, a back-trajectory analysis, an aerosol model and a radiative transfer model. Back-trajectories, wind fields and aerosol model analyses show that the first dust originated in northern/northwestern China and the second generated in the Taklimakan desert in northwest China, and traveled across the Hexi corridor and Loess Plateau to the Yangtze Delta region (the so-called “dust corridor”). The mean lidar extinction-to-backscatter ratio (LR) during the two dust events was 38.7 ± 10.4 sr and 42.7 ± 15.2 sr, respectively. The mean aerosol depolarization ratio ( δ a) for the first dust event was 0.16 ± 0.07, with a maximum value of 0.32. For the second, the mean δ a was around 0.19 ± 0.06, with a maximum value of 0.29. Aerosol extinction coefficient and δ a profiles for the two events were similar: two aerosol layers consisting of dust aerosols and a mixture of dust and anthropogenic pollution aerosols. The topmost aerosol layer is above 3.5 km. The maximum mean aerosol extinction coefficients were 0.5 km 611 and 0.54 km 611 at about 0.7 km and 1.1 km, respectively. Significant effects of cooling at the surface and heating in the atmosphere were found during these dust events. Diurnal mean shortwave radiative forcings (efficiencies) at the surface, the top-of-the-atmosphere and within the atmosphere were026136.8 (6180.0),026113.6 (6129.6) and 23.2 (50.4) W m 612, respectively, during the first dust event, and026148.2 (6170.9),026121.4 (6131.5) and 26.8 (39.4) W m 612, respectively, during the second dust event. Maximum heating rates occurred at 0.7 km during the first dust event and at 1.1 km during the second dust event, with a maximum value of 2.74 K day 611 for each case. This significant atmospheric heating induced by elevated dust aerosol layers can affect convection and stability in the lower troposphere.      URL     [Cited within:1] [29] Liu J. J., Y. F. Zheng, Z. Q. Li, and M. Cribb, 2011b: Analysis of cloud condensation nuclei properties at a polluted site in southeastern China during the AMF-China Campaign.J. Geophys. Res.,116,D00K35,https://doi.org/10.1029/2011JD016395. [1] Cloud condensation nuclei (CCN) measurements are essential to understanding cloud processes but CCN measurements are scarce. This study analyzes CCN measurements acquired at Shouxian, a polluted site in southeastern China, from August 1090009October 31, 2008 during the deployment of the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF). The ranges of daily mean condensation nuclei concentrations (NCN) were approximately 310009000912000, 23000900097400, and 426009000915500 cm0908083 in August, September, and October, respectively; the corresponding ranges of CCN concentrations (NCCN) at 0.49% supersaturation were about 19600900095670, 17700900093530, and 15000900095700 cm0908083. The average ratio of NCCN/NCN was 0.04, 0.12, 0.35, 0.53, 0.65, 0.69 and 0.72 for supersaturation values of 0.08%, 0.20%, 0.34%, 0.49%, 0.78%, 1.07% and 1.37%, respectively. NCN and NCCN peaked in the early morning and late afternoon, when human activities were most intense. CCN were more abundant in air masses influenced by anthropogenic pollution from densely populated areas. NCCN was proportional to NCN, but NCCN/NCN decreased with increasing NCN. There was a good correlation between NCCN (at 0.49% supersaturation) and aerosol optical depth (AOD) (500 nm), that is especially strong for fine-mode aerosols (Angstrom exponent () > 0.8). This relationship can be fitted with a power law function. The changes of NCCN with various factors are explained. A dust event was identified showing a significant increase in NCN and a dramatic decrease in the NCCN/NCN ratio, implying that dust particles do not increase NCCN much, despite mixing with other anthropogenic aerosols.      URL     [Cited within:2] [30] Liu J. J., Y. F. Zheng, Z. Q. Li, C. Flynn, and M. Cribb, 2012: Seasonal variations of aerosol optical properties,vertical distribution and associated radiative effects in the Yangtze Delta region of China.J. Geophys. Res.,117,D00K38,https://doi.org/10.1029/2011JD016490. [1] Four years of columnar aerosol optical properties and a one-year vertical profiles of aerosol particle extinction coefficient at 527 nm are analyzed at Taihu in the central Yangtze River Delta region in eastern China. Seasonal variations of aerosol optical properties, vertical distribution, and influence on shortwave radiation and heating rates were investigated. Multiyear variations of aerosol optical depths (AOD), 0105ngstrom exponents, single scattering albedo (SSA) and asymmetry factor (ASY) are analyzed, together with the vertical profile of aerosol extinction. AOD is largest in summer and smallest in winter. SSAs exhibit weak seasonal variation with the smallest values occurring during winter and the largest during summer. The vast majority of aerosol particles are below 2 km, and about 62%, 67%, 67% and 83% are confined to below 1 km in spring, summer, autumn and winter, respectively. Five-day back trajectory analyses show that the some aerosols aloft are traced back to northern/northwestern China, as far as Mongolia and Siberia, in spring, autumn and winter. The presence of dust aerosols were identified based on the linear depolarization measurements together with other information (i.e., back trajectory, precipitation, aerosol index). Dust strongly impacts the vertical particle distribution in spring and autumn, with much smaller effects in winter. The annual mean aerosol direct shortwave radiative forcing (efficiency) at the bottom, top and within the atmosphere are 09080834.8 00± 9.1 (09080854.4 00± 5.3), 0908088.2 00± 4.8 (09080813.1 00± 1.5) and 26.7 00± 9.4 (41.3 00± 4.6) W/m2 (Wm0908082 03040908081), respectively. The mean reduction in direct and diffuse radiation reaching surface amount to 109.2 00± 49.4 and 66.8 00± 33.3 W/m2, respectively. Aerosols significantly alter the vertical profile of solar heating, with great implications for atmospheric stability and dynamics within the lower troposphere.      URL     [Cited within:4] [31] Liu J. J., Z. Q. Li, Y. F. Zheng, J. C. Chiu, F. S. Zhao, M. Cadeddu, F. Z. Weng, and M. Cribb, 2013: Cloud optical and microphysical properties derived from ground-based and satellite sensors over a site in the Yangtze Delta region.J. Geophys. Res.,118,9141-9152,https://doi.org/10.1002/jgrd.50648. [1] Comprehensive surface-based retrievals of cloud optical and microphysical properties were made at Taihu, a highly polluted site in the central Yangtze Delta region, during a research campaign from May 2008 to December 2009. Cloud optical depth (COD), effective radius (Re), and liquid water path (LWP) were retrieved from measurements made with a suite of ground-based and spaceborne instruments, including an Analytical Spectral Devices spectroradiometer, a multifilter rotating shadowband radiometer, a multichannel microwave radiometer profiler, and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua satellites. Retrievals from zenith radiance measurements capture better the temporal variation of cloud properties than do retrievals from hemispherical fluxes. Annual mean LWP, COD, and Re are 115.809000900±09000990.8090009g/m2, 28.509000900±09000919.2, and 6.909000900±0900094.20900090008m. Over 90% of LWP values are less than 250090009g/m2. Most of the COD values (>90%) fall between 5 and 60, and ~80% of Re values are less than 100900090008m. Maximum (minimum) values of LWP and Re occur in summer (winter); COD is highest in winter and spring. Raining and nonraining clouds have significant differences in LWP, COD, and Re. Rainfall frequency is best correlated with LWP, followed by COD and Re. Cloud properties retrieved from multiple ground-based instruments are also compared with those from satellite retrievals. On average, relative to surface retrievals, mean differences of satellite retrievals in cloud LWP, COD, and Re were 09080833.6090009g/m2 (09080826.4%), 0908085.8 (09080831.4%), and 2.90900090008m (29.3%) for 11 MODIS-Terra overpasses and 09080843.3 g/m2 (09080822.3%), 0908083.0 (09080810.0%), and 0908081.30900090008m (09080812.0%) for 8 MODIS-Aqua overpasses, respectively. These discrepancies indicate that MODIS cloud products still suffer from large uncertainties in this region.      URL     [Cited within:2] [32] Liu J. J., Z. Q. Li, Y. F. Zheng, and M. Cribb, 2015: Cloud-base distribution and cirrus properties based on micropulse lidar measurements at a site in southeastern China.Adv. Atmos. Sci.,32(7),991-1004,https://doi.org/10.1007/s00376-014-4176-2.      URL     [Cited within:1] [33] Liu J. J., Z. Q. Li, and M. Cribb, 2016: Response of marine boundary layer cloud properties to aerosol perturbations associated with meteorological conditions from the 19-Month AMF-Azores campaign.J. Atmos. Sci.,73(11),4253-4268,https://doi.org/10.1175/JAS-D-15-0364.1. Abstract This study investigates the response of marine boundary layer (MBL) cloud properties to aerosol loading by accounting for the contributions of large-scale dynamic and thermodynamic conditions and quantifies the first indirect effect (FIE). It makes use of 19-month measurements of aerosols, clouds, and meteorology acquired during theAtmospheric Radiation Measurement Mobile Facility field campaign over the Azores. Cloud droplet number concentrations Nc and cloud optical depth (COD) significantly increased with increasing aerosol number concentration Na. Cloud droplet effective radius (DER) significantly decreased with increasing Na. The correlations between cloud microphysical properties [Nc, liquid water path (LWP), and DER] and Na were stronger undermore stable conditions. The correlations between Nc, LWP, DER, and Na were stronger under ascendingmotion conditions, while the correlation between COD and Na was stronger under descending-motion conditions. The magnitude and corresponding uncertainty of the FIE (=[- ln(DER)/ ln(Na)] at constant LWP) ranged from 0.060 0.022 to 0.101 0.006 depending on the different LWP values. Under more stable conditions, cloud-base heights were generally lower than those under less stable conditions. This enabled a more effective interaction with aerosols, resulting in a larger value for the FIE. However, the dependence of the response of cloud properties to aerosol perturbations on stability varied according to whether ground- or satellite-based DER retrievals were used. The magnitude of the FIE had a larger variation with changing LWP under ascending-motion conditions and tended to be higher under ascending-motion conditions for clouds with low LWP and under descending-motion conditions for clouds with highLWP.Acontrasting dependence of FIE on atmospheric stability estimated fromthe surface and satellite cloud properties retrievals reported in this study underscores the importance of assessing all-level properties of clouds in aerosol-cloud interaction studies.      URL     [Cited within:2] [34] Ma J. Z., Y. Chen, W. Wang, P. Yan, H. J. Liu, S. Y. Yang, Z. J. Hu, and J. Lelieveld, 2010: Strong air pollution causes widespread haze-clouds over China.J. Geophys. Res.,115,D18204,https://doi.org/10.1029/2009JD013065. [1] North China (Huabei in Chinese) is a geographical region located between 3200°N and 4200°N latitude in eastern China, including several provinces and large municipalities (e.g., Beijing and Tianjin). In the past decades the region has experienced dramatic changes in air quality and climate. Among the multiple causes aerosol pollution is expected to play a particularly important role. To investigate this, a field measurement campaign was performed in April090009May 2006 as part of the project Influence of Pollution on Aerosols and Cloud Microphysics in North China. Here we report the first aircraft measurements of atmospheric trace gases, aerosols, and clouds over this part of China, a region strongly affected by both natural desert dust and pollution smog. We observed very high concentrations of gaseous air pollutants and haze particles, partly together with nonprecipitating stratiform clouds. The clouds were characterized by numerous droplets, much smaller than in a less-polluted atmosphere. Our data reveal that the highly efficient coating of dust particles by pollution acids provides the predominant source of cloud condensation nuclei. The pollution-enhanced activation of dust particles into droplets is shown to be remarkably efficient so that clouds even form below 100% relative humidity. Contrary to previous analyses, we find that the haze particles influence the spectral shape of the cloud droplet size distribution such that the indirect climate cooling effect of aerosols on clouds is increased. The widespread haze, combined with low clouds, diminishes air quality and exerts an unusually strong cooling forcing on climate.      URL     [Cited within:1] [35] Marshak A., Y. Knyazikhin, K. D. Evans, and W. J. Wiscombe, 2004: The "RED versus NIR" plane to retrieve broken-cloud optical depth from ground-based measurements. J. Atmos. Sci., 61, 1911-1925, https://doi.org/10.1175/1520-0469 (2004)061<1911:TRVNPT>2.0.CO;2. A new method for retrieving cloud optical depth from ground-based measurements of zenith radiance in the red (RED) and near-infrared (NIR) spectral regions is introduced. Because zenith radiance does not have a one-to-one relationship with optical depth, it is absolutely impossible to use a monochromatic retrieval. On the other side, algebraic combinations of spectral radiances, such as normalized difference cloud index (NDCI), while largely removing nonuniqueness and the radiative effects of cloud inhomogeneity, can result in poor retrievals due to its insensitivity to cloud fraction. Instead, both RED and NIR radiances as points on the “RED versus NIR” plane are proposed to be used for retrieval. The proposed retrieval method is applied to Cimel measurements at the Atmospheric Radiation Measurements (ARM) site in Oklahoma. Cimel, a multichannel sun photometer, is a part of the Aerosol Robotic Network (AERONET)—a ground-based network for monitoring aerosol optical properties. The results of retrieval are compared with the ones from microwave radiometer (MWR) and multifilter rotating shadowband radiometer (MFRSR) located next to Cimel at the ARM site. In addition, the performance of the retrieval method is assessed using a fractal model of cloud inhomogeneity and broken cloudiness. The preliminary results look very promising both theoretically and from measurements.
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Key words
ground-based measurements
aerosol indirect effect
southeastern China

Authors
Jianjun LIU
Zhanqing LI