First Surface-based Estimation of the Aerosol Indirect Effect over a Site in Southeastern China

Jianjun LIUand Zhanqing LI

1Earth System Science Interdisciplinary Center,University of Maryland,College Park,MD 20740,USA

2State Laboratory of Earth Surface Process and Resource Ecology,College of Global Change and Earth System Science,Beijing Normal University,Beijing 100875,China

First Surface-based Estimation of the Aerosol Indirect Effect over a Site in Southeastern China

Jianjun LIU?1and Zhanqing LI1,2

1Earth System Science Interdisciplinary Center,University of Maryland,College Park,MD 20740,USA

2State Laboratory of Earth Surface Process and Resource Ecology,College of Global Change and Earth System Science,Beijing Normal University,Beijing 100875,China

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(FIECER)and COD(FIECOD)with changes in AI for different seasons and air masses.FIECODand FIECERare 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.

ground-based measurements,aerosol indirect effect,southeastern China

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 ref l ects 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?2for 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(Br′eon 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(V′arnai 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 inf l uence 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 studytheinf l uenceofaerosolsonradiativef l uxes,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 inf l uence 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 signif icantly.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:

where F673,obsrepresents 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?2for LWP＜200 g m?2and～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:

where τ represents COD,rerepresents CER,and ρwis 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＞25μmor CER＜2μm are excludedbecause these are unrealistic values for low-level warm clouds(Bulgin et al.,2008).Also,cloudswithLWP＜20gm?2andLWP＞700 g m?2are excluded to remove unaccounted-for biases and rainy periods,respectively(Min et al.,2003;Liu et al.,2013).To examine the inf l uence of aerosols on non-precipitating cloud properties,only clouds with LWP＜300 g m?2are analyzed(Chiu et al.,2012;Harikishan et al.,2016).

2.2.Aerosol properties

Total scattering coefficients(σ)of aerosol particles with diameters＜1μm and＜10μm were measured by two threewavelength(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 CO2gas to ensure the accuracy of measurements.The uncertainty in nephelometer-measured σ ranges from 1–4 Mm?1for 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 ?ngstr¨om 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 classifiedintofourmajorclusters:airmassesoriginatingfromthe 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 5μm(Fig.1f).

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)inf l uences 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?2for all air masses.The PDFs of CER for each air mass in Fig.2f have similar shapes.A shift in CER toward larger valuesfrom air mass I to air mass IV is apparent.

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.

Fig.1.PDFs of aerosol and cloud properties in JJA(blue lines),SON(green lines)and December(red lines):(a)scattering coefficient(σ);(b)?ngstr¨om 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.

3.1.3.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 mix-ing 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,suggestingthattheaerosolparticlesizedoesnotchangemuch 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.Inf l uence of aerosols on cloud properties

3.2.1.Relationship between cloud properties and AI for different seasons

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

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 con fidence level of the mean values if independent data are assumed,and are calculated as where sd2is 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.

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 largescale dynamic(e.g.,vertical velocity)and thermodynamic(e.g.,lower-tropospheric stability)parameters,show no sig-nificant 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 10.3μm to 9.4μm)in JJA and by～36%(from 7.8μm to 5.0μm)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 Br′eon,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).

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?2to 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?2to 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.

DuringtheJJAperiod,CODincreaseswithincreasingAI,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 ref l ection of solar radiation by clouds.

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 signi ficant difference as AI increases.Under both air mass conditions,CER signi ficantly decreases as AI increases,suggesting a strong infl uence of aerosols on cloud microphysical properties.The mean CER decreases by～34%(from 5.8 to 3.8μm)and by～22%(from 8.1 to 6.3μm)under air mass II and III conditions,respectively.No signi ficant 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 signi ficant 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.

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

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,inf l uence the site.The slope of the COD–AI exponential regression line is larger for samples inf l uenced by air mass II(0.15)than for those inf l uenced 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(ΔCER),LWP(ΔLWP),and COD(ΔCOD)for the lowest(ΔAIL)and highest(ΔAIH)quartile of percentage changes in AI.The percentage change in hourly mean cloud and aerosol properties is calculated as:

where M represents an aerosol or cloud variable,ΔM is thepercentage change in the variable, is the mean value over an hour of the variable,and i is the LST.When i=9,i?1represents the mean value of the variable from 0800 LST to0900 LST,and 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 ΔAILand ΔAIHare?0.26 and 0.22,respectively,which represents a decrease by 26%and an increase by 22%,respectively.The PDF of ΔCER in Fig.6a shows that there is a shift in ΔCER toward positive values for the ΔAILcase and toward negative values for the ΔAIHcase.For the ΔAILcase,the mean ΔCER is 0.18(an increase of 18%),and for the ΔAIHcase the mean ΔCER is?0.06(a decrease of 6%).A negative relationship between ΔCER and ΔAI is found,which suggests that when AI increases after an hour,the CER tends to decrease simultaneously.Figures 6b and c show that ΔLWP and ΔCOD are positively correlated with ΔAI.For the ΔAILcase,there are more samples with ΔLWP ＜ 0 and ΔCOD ＜ 0 than with ΔLWP ＞ 0 and ΔCOD ＞ 0,but for the ΔAIHcase 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 ΔAI,the smaller the ΔCER and the larger the ΔLWP and ΔCOD.

3.4.Aerosol FIE

The aerosol FIE can be calculated as

where α is the CCN concentration or a CCN proxy,such as aerosolnumberconcentration,AOD,etc.Inthecurrentstudy,aerosol and cloud measurements were first separated into different LWP bins ranging from 20 to 300 g m?2in 20 g m?2increments.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 FIECERand FIECOD,respectively.

Figure 7 shows the magnitude of FIECERin 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 FIECERthat is statistically significant at the 95%confidence level are considered.The mean value of FIECERis 0.16±0.06 and 0.17±0.06 in JJA and SON,respectively,which suggests that FIECERhas no systematically strong seasonal variation at the SX site.The mean FIECERvalues under air mass II and III conditions(0.23±0.09 and 0.20±0.06,respectively)are not significantly different.The FIECERin 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 FIECERincreases and then slightly decreases as LWP increases.Figure 8 shows the magnitude of FIECODfor 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 FIECODis similar to the FIECERin each season and under each air mass condition.The variation in FIECODwith increasing LWP is also consistent with that of FIECERin each season and under each air mass condition.

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

Fig.7.Magnitudes of the first indirect effect(FIE)(bars)estimated from changes in CER with changes in AI(FIECER)in each LWP bin from 20 to 300 g m?2in 20 g m?2intervals 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 FIECERthat 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(FIECOD).

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).Groundbased 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 discrepancieswithresultsderivedfromsurfacemeasurements.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 f l agged 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＜25μm 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 f l uctuating 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.

Results show that the effect of aerosols on cloud microphysical properties retrieved from satellite measurements are weaker,even contrary to the Twomey effect,and are affected by much more noise,compared with the results from surfacebased measurements.One reason is that a passive remote sensing instrument like MODIS cannot measure aerosol and cloud properties simultaneously because clouds block signals from aerosols located beneath them.This introduces some uncertainties when analyzing the relationship between aerosol and cloud microphysical properties.A second reason is that satellite-retrieved CER typically represents the cloud particle size near the top of optically thick clouds,while surface-retrieved CER,weighted by the water mass in the cloud,represents the layer mean particle size.Under the same aerosol loading conditions,the MODIS-derived CER is significantly larger than the CER estimated from surface measurements(figure not shown),which possibly masks the inf l uence of aerosols on cloud properties.In addition,since most aerosol particles are found within the boundary layer over this region(Liu et al.,2012),the CER derived from sur-face measurements may be inf l uenced more by aerosols than theCERderivedfromsatellitemeasurementsbecausethereis more contact between cloud particles near the cloud base and aerosols(Liu et al.,2016).A third possible reason involves the uncertainties in AOD retrievals due to cloud contamination.A study by V′arnai and Marshak(2014)revealed that satellite-retrieved AOD for roughly half of the pixels within 5 km of clouds can be up to 50%greater than the AOD for pixels further away from clouds.Thus,satellite retrievals made near clouds can lead to spurious correlations between aerosol and cloud parameters(Costantino and Br′eon,2013).Also,the AOD retrieved from MODIS measurements does not represent real aerosol loading in the atmosphere because the retrieval is enhanced by aerosol swelling effects(Jeong and Li,2010).The relationship between AOD and CCN is signif icantly inf l uenced by RH(Liu and Li,2014).

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

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 inf l uence of aerosols on radiative f l uxes,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 inf l uences 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)inf l uence the site,CER(COD)significantly decreases(increases)with increasing AI.

There is a shift in percentage changes in hourly mean CER(ΔCER)towardpositiveandnegativevaluesforthelowest(ΔAIL)and highest(ΔAIH)quartile of percentage changes in AI cases,respectively.For the ΔAILcase,the mean ΔCER is 0.18(an increase of 18%),and for the ΔAIHcase the mean ΔCER 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 FIECERin 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)inf l uences the site.The magnitudes of mean FIECODin summer and fall and for each air mass type are similar to those of FIECER.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 spacebasedperspective.Theeffectofaerosolsoncloudmicrophysical 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 inf l uenced 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,Office of Science,Office 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).

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24 April 2017;revised 15 August 2017;accepted 18 September 2017)

:Liu,J.J.,and Z.Q.Li,2018:First surface-based estimation of the aerosol indirect effect over a site in southeastern China.Adv.Atmos.Sci.,35(2),169–181,https://doi.org/10.1007/s00376-017-7106-2.

?Corresponding author:Jianjun LIU

E-mail:jianjun5212@163.com

?Institute of Atmospheric Physics/Chinese Academy of Sciences,and Science Press and Springer-Verlag GmbH Germany,part of Springer Nature 2018