Low-Level Temperature Inversions and Their Effect on Aerosol Condensation Nuclei Concentrations under Different Large-Scale Synoptic Circulations

LI Jun,CHEN Hongbin∗,Zhanqing LI,WANG Pucai,Maureen CRIBB,and FAN Xuehua

1Key Laboratory of Middle Atmosphere and Global Environment Observation,Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing 100029

2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science&Technology,Nanjing 210044

3The Earth System Science Interdisciplinary Center,University of Maryland,College Park,MD 20740,USA

4College of Global Change and Earth System Science,Beijing Normal University,Beijing 100875

Low-Level Temperature Inversions and Their Effect on Aerosol Condensation Nuclei Concentrations under Different Large-Scale Synoptic Circulations

LI Jun1,2,CHEN Hongbin∗1,2,Zhanqing LI3,4,WANG Pucai1,Maureen CRIBB3,and FAN Xuehua1,2

1Key Laboratory of Middle Atmosphere and Global Environment Observation,Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing 100029

2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science&Technology,Nanjing 210044

3The Earth System Science Interdisciplinary Center,University of Maryland,College Park,MD 20740,USA

4College of Global Change and Earth System Science,Beijing Normal University,Beijing 100875

Knowledge of the statistical characteristics of inversions and their effects on aerosols under different large-scale synoptic circulations is important for studying and modeling the diffusion of pollutants in the boundary layer.Based on results generated using the self-organizing map(SOM)weather classif i cation method,this study compares the statistical characteristics of surface-based inversions(SBIs)and elevated inversions(EIs),and quantitatively evaluates the effect of SBIs on aerosol condensation nuclei(CN)concentrations and the relationship between temperature gradients and aerosols for six prevailing synoptic patterns over the the Southern Great Plains(SGP)site during 2001–10.Large-scale synoptic patterns strongly inf l uence the statistical characteristics of inversions and the accumulation of aerosols in the low-level atmosphere.The activity, frequency,intensity,and vertical distributionof inversions are signif i cantly different among these synoptic patterns.Thevertical distribution of inversions varies diurnally and is signif i cantly different among the different synoptic patterns.Anticyclonic patterns affect the accumulation of aerosols near the ground more strongly than cyclonic patterns.Mean aerosol CN concentrations increase during SBIs compared to no inversion cases by 16.1%,22.6%,24.5%,58.7%,29.8%and 23.7%for the six synoptic patterns.This study conf i rms that there is a positive correlation between temperature gradients and aerosol CN concentrations near the ground at night under similar large-scale synoptic patterns.The relationship is different for different synoptic patterns and can be described by linear functions.These f i ndings suggest that large-scale synoptic patterns change the static stability of the atmosphere and inversions in the lower atmosphere,thereby inf l uencing the diffusion of aerosols near the ground.

temperature inversion,aerosol,condensation nuclei,large-scale synoptic pattern,statistical characteristics

1.Introduction

Low-level atmospheric temperature inversions,where atmospheric temperature increases with altitude,occur frequentlyatmiddleandhighlatitudes.Theyinf l uencethedepth of vertical mixing,the surface radiation balance,the diffusion of pollutants,and cloud formation in the boundary layer (Fedorovich et al.,1996;Wendisch et al.,1996;Dong et al., 2005;Janhall et al.,2006;Wallace et al.,2010).An inversion in the lower atmosphere creates stable atmospheric conditions,which restricts the diffusion of industrial and humanproduced smoke,and harmful gases and aerosols.These pollutants accumulatenear the ground,and usually lead to air pollution episodes.Over the past century,several air pollution episodes of major signif i cance have occurred around the world and have led to thousands of deaths.Temperature inversionswere mainly responsibleforthese events(Watanabe, 1998).

Investigationoftheeffectsoflow-leveltemperatureinversions on pollutants not only helps us understand the impact of inversions on environmental changes,but also provides a reliable basis for effectively predicting pollution episodes and controlling pollutant emissions.Pollutant gases near the ground,such as CO,NO,NO2,SO2and O3,signif i cantly increase if a temperature inversion,especially a morning time surface-based temperature inversion(SBI),is present(Holzworth,1972;Janhall et al.,2006).Aerosols,an importantkind of air pollutant,affect human health,and f i ne particulate matter(PM2.5)mass loadings have been statistically correlated with morbidity and mortality(Pope et al.,1993).The increase in aerosol particle concentrations near the ground is also highly correlated with the presence of temperature inversions(Malek et al.,2006).Wallace et al.(2010)studied the effect of temperature inversions on groud-level NO2and aerosols,and reported increases of 49%and 54%in NO2and PM2.5,respectively,during nighttime inversion episodes. However,which temperature inversion parameters affect the accumulation of aerosols,and the relationship between these parameters and aerosols is still uncertain.Milionis and Davies(1992)foundthat theintensityofinversionsis directly proportional to their ability to inhibit the vertical movement of pollutants.This information is important for modeling the diffusion and transport of air pollutants.Most models of air pollutant diffusion already incorporate characteristics of the capping inversion at the top of the atmospheric boundary layer(ABL)(Milionis and Davies,2008).However,little attention has been paid to the quantitative assessment of the relationship between inversion parameters and aerosol concentrations.

Serious pollution episodes do not generally result from sudden increases in the emission of pollutants,but rather from certain meteorological conditions that diminish the ability of the atmosphere to disperse pollutants(Cheng et al.,2007).There is a causal relationship between prevailing weather conditions and the statistics describing atmospheric temperature inversions(Milionis and Davies,2008).Local surface conditions can counteract atmospheric dynamics in regulating inversion activity and air quality.Bailey et al.(2011)analyzed temperature inversion characteristics in the U.S.Southwest and relationships to 500 hPa large-scale atmospheric circulation,and found that dynamical changes withclimatedidnotuniformlyinf l uenceinversionsandurban air qualityconditionsin that region.Pearce et al.(2011)identif i ed 20 large-scale synoptic patterns over Australia by applying the self-organizing map(SOM)method to daily mean sea level pressure reanalysis data.They found links between synoptic-scale sea level circulation and observed changes in air pollutant concentrations,and that individual synoptic categories had different effects on air pollutants.These studies indicate that changes in large-scale weather patterns can affect low-level temperature inversion statistics and air quality, and that the presence of temperature inversions also prevents pollutants from dispersing.Yet it remains unclear whether the effect of temperature inversion on pollutants is significantly different for different synoptic patterns.The SGP (Southern Great Plains)Central Facility(CF)is located near Lamont in north central Oklahoma and is home to a suite of specializedin situand remote sensing instruments gathering atmospheric data of unprecedented quality,consistency,and completeness.All aerosol and radiosonde data used in this work are from this site.The SGP CF is surrounded by farmland and is distant from large urban areas.However,there are several small cities,one power plant,one oil ref i nery, and an interstate highway within 50 km of the site.So,atmospheric aerosols over the site can be considered representative of a mixed regional aerosol type.The daily aerosol condensation nuclei(CN)concentrations cycle is strong at the SGP CF.The average aerosol CN concentration near the groundislowandconstantduringthenight,quicklyincreases during the two hours after sunrise,and reaches its peak in the midafternoon.Possible explanations for the high daytime aerosol CN concentration levels include sulfate formation from SO2emissions from local power plants or oil refi neries,the photochemically-driven production of organic or N-containing particles,regional-scale agricultural burning or transportation activities,and/or diurnal convective mixing and stability cycles(Sheridan et al.,2001).

The purpose of this work is to quantify inversion activity and its effect on aerosols,and to deduce the relationship between inversion parameters and aerosol CN concentrations for a set of prevailing synoptic patterns over the continental United States.Section 2 brie fl y describes the data and the method used in this study.The main results are presented in section 3.Conclusions are given in section 4.

2.Data and methods

2.1.Data

Ten years(2001–10)of continuous and high-quality routine radiosonde data measured at the SGP CF are used in this study.These measurements are carried out under the aegis of the U.S.Department of Energy’s Atmospheric Radiation Measurement Program(www.arm.gov).A Vaisala sounding unit is used to detect vertical pro fi les of pressure, temperature,relative humidity,and wind speed and direction,four times a day at 0530,1130,2030 and 2330 UTC. The pro fi ling vertical resolution is～10 m(Holdridge et al.,2011).For the purpose of calculating nighttime and daytime temperature inversion parameters,pro fi les generated from the 2330 UTC sounding represent daytime profi les and 0530/1130 UTC soundings represent nighttime pro fi les.

Simultaneous ground measurements of aerosol CN and totalnumberconcentrationsforparticleswithdiametersranging from 10 nm to 3µm made at a one-minutetime resolution are also used(Jefferson,2011).

For characterizing large-scale synoptic patterns,the daily NationalCenters for EnvironmentalProtection(NCEP)mean sea level pressure(MSLP)records from the North American Regional Reanalysis(NARR)are used.The MSLP fi eld is a commonly used proxy for atmospheric circulation because it relates well to the spatial pattern of large-scale synoptic processes(Huth et al.,2008).Corresponding daily 500 hPa geopotentialheights are also used in the comprehensiveanalysis presented in this study.The resolution of NCEP NARR products is 32 km×32 km and the domain extends from 30°N to 50°N,and from 75°W to 120°W(http://www.esrl. noaa.gov).

2.2.Algorithm for detecting inversions and the SOM method for weather classi fi cation

This study focuses on low-level inversions with bases below 2000m.SBIs are inversionswith bases below 100 m and elevated inversions(EIs)are inversions with bases above 100 m.The fi rst-derivative algorithm is used here for detecting inversions(Kahl,1990;Serreze et al.,1992).The derivative of each temperature pro fi le is fi rst calculated.Then,the profi le of these derivatives is scanned upward from the surface to the 2000 m level.An inversion layer is identi fi ed when the derivative is positive and remains positive over an altitude range of at least 100 m.Very thin negative derivative layers are occasionally encountered.If these layers are less than 100 m,then the thin layers are considered as embedded within the overall inversion layer.

The SOM is an arti fi cial neural network algorithm used for clustering,visualization,and abstraction(Kohonen, 1998).It offers an alternative approach to synoptic climatology by providing a mechanism for visualizing the complex distribution of synoptic states while treating the data as a continuum(Hewitson and Crane,2002).This technique has beenincreasinglyused tosuccessfullystudyrelationships between local meteorological conditions and the large-scale climatology.The SOM algorithm identi fi es groups in such a way that minimizes the within-group differences while maximizing the between-group differences.An advantage of this method is that nodes are distributed in a nonlinear fashion. As a result,a larger number of climatological groupings are created near areas of high-density data,allowing one to represent the variability in the original time series of geopotential height records more robustly(Bailey et al.,2011).The use of a small dimension will result in a map that provides a broad generalization of the input data,while a large dimension will result in a map with types that may be quite similar to adjacent types(Cassano et al.,2006).The output dimension selected in this study is based on previous work done on North American weather classi fi cation(Hewitson and Crane,2002;Bailey et al.,2011).Ten years of daily MSLP maps were organized into a 5×5 output dimension through application of the SOM algorithm.The atmospheric state is partitioned into 25 categories associated with largescale synopticpatterns to provideinsight into the in fl uenceof large-scale synoptic processes on temperature inversions and aerosols.

The SOM of the MSLP provides a clear visualization of the atmospheric continuum affecting the SGP CF by presenting25archetypesof thesynopticstate thatcharacterizelargescale atmospheric circulation over the region.The most frequent and representative synoptic patterns are the cyclonic center(CC,also denoted as 00,7.6%),the weak pressure (WP,also denoted as 02,6.3%),the cyclonic bottom(CB, also denoted as 04,4.7%),the cold front(CF,also denoted as 32,4.7%),the anticyclone edge(AE,also denoted as 34, 5.5%),and the anticyclone center(AC,also denoted as 43, 6.3%).These prevailing synoptic patterns form the basis of the following analysis in this work.Figure 1 shows annual MLSP patterns and their corresponding500 hPa geopotential height patterns for the six synoptic patterns averaged over 2001 to 2010.In the CC scenario,the SGP CF is located within the area of lowest atmospheric surface pressure(Fig. 1a).A southwesterly fl ow at the 500 hPa level is seen and is associated with strong convergence and ascending motion in the lowest part of the atmosphere.Weather conditions are mainly overcast with frequent rainfall.In the WP scenario,the SGP CF is within an area of weak depression at the surface and under a 500 hPa ridge(Fig.1b).Cloudy skies dominate.The SGP CF is located along the edge of a strong depression and near a strong anticyclone in the CB scenario (Fig.1c).Conditions at the 500 hPa level are similar to those seen in the CC scenario and cloudy skies prevail.Figure 1d shows the CF scenario where the SGP CF is located in the transition zone between warm and cold air masses.Weather conditions are generally rainy and windy with clearing after passage of the front.In the AE scenario,the SGP CF is situated at the rear of a strong anticyclone and the dominant airfl ow atthe 500hPalevelis fromthewest(Fig.1e).Theskyis clear or contains scattered clouds.The SGP CF is located in the center of anticyclone subsidence in the AC scenario(Fig. 1f).There is a northwesterly fl ow at the 500 hPa level associated with strong divergence in the lower atmosphere.Clear skies dominate.

3.Results

3.1.Inversion characteristics

Inordertoquantitativelyexpresstherelationshipbetween inversion activity and prevailing patterns,a temperature inversion activity index(TIAI)is def i ned as the product of“mean intensity of inversions(the difference in the potential temperature across the inversion)and average number of inversions per prof i le where at least one inversion is identif i ed and proportionof temperatureprof i les with at least one inversion is identif i ed”,i.e.,

wheredθdenotes the difference in the potential temperature across the inversion,Kis the corresponding total number of inversions,irepresents one of the corresponding inversions,Nis the total number of available vertical temperature inversions,andnis the corresponding number of prof i les with at least one inversion(Milionis and Davies,2008).

According to the synoptic atmospheric classif i cation,the frequencies of the SBI,EI and no-inversion(NIN),and the parameters of the temperature inversions,are calculated for each synoptic pattern using the corresponding temperature prof i les.For some prof i les,two,three,or four inversions are detected;the SBI and EI occurred together,and all SBI and EI values are considered in this study.

Aone-wayanalysisofvariance(ANOVA)isperformedto examinewhether the mean values of inversionparametersarestatistically signif i cantly different among the six prevailing patterns def i ned above.Fisher’s least signif i cant difference (LSD)test(Fisher,1966)is also applied,and all differences of pairs of class means are compared with LSDs.A signif icance level of 0.05 is used.In this work,our null hypothesis (H0)is that there is no difference in the mean inversion parameters or aerosol concentrations among the six prevailing patterns(groups).H0is tested using theF-statistic.When H0is rejected,one-way ANOVA can only determine that mean inversionparametersor aerosolconcentrationsamonggroups are signif i cantly different,but it does not necessarily conf i rm that everyset of two groupsis suff i cientlydifferent.The LSD test is a type of multiple comparison test that can investigate this difference between groups.For the application of this test,all differences of two groups are compared using the LSD.When the difference between two group means is greater than the LSD,the two groups are signif i cantly different at theαsignif i cance level(0.05).

Table 1 shows the frequencies of occurrence of SBIs and no inversions(NOIs)and the TIAI index of SBIs for the six synoptic patterns.SBIs occur more frequently under anticyclonic patterns(AE:46.0%;AC:38.3%)and less frequently under cyclonic patterns(CB:26.6%;CC:31.6%)and when a cold front is present(CF:22.8%).For cyclonic patterns and weak pressure,the frequency of occurrence of NOIs is high (more than 30%)and that of anticyclonic patterns and cold frontis low(less than 20%).Mean TIAI index values of SBIs for the AE and AC patterns are almost twice those for the CB and CF patterns.Table 2 shows mean variances of the SBIs and results from the one-way ANOVA and the LSD test for the six synoptic patterns.Differences in the mean values of temperature difference and temperature gradient across the inversions for the six synoptic patterns are statistically signif i cant at the 95%conf i dence level(F-values are 17.5 and 32.3,respectively).Differences in mean SBI depths among the different synoptic patterns are small,except for the AC pattern(shaded in gray),which is signif i cantly thinner than the other f i ve patterns.The anticyclonic patterns have the highest values of mean temperature difference across inversions and temperature gradients(shaded in black)and the lowest values are seen for the WP pattern(shaded in gray). In general,anticyclonic patterns favor the formation of SBIs. This pronounced difference may have arisen because anticyclonicpatternsare associatedwith divergenceandsubsidence near the surface,which increases static stability in the lower atmosphere.The 500 hPa airf l ow over the SGP CF was from the northwest,bringing in cold and dry air.Under these conditions,clear skies dominated,leading to strong infrared radiation cooling near the ground,which is conducive to the formation and evolution of SBIs.Cyclonic patterns are associated with convergence and ascent near the ground,which decreases static stability in the lower atmosphere.The moist southwesterly air f l ow at the 500 hPa level leads to cloudy skies with frequent rainfall over the SGP CF.This is generally not conducive to the formation of SBIs.During and after the passage of a cold front at the SGP CF,inclement weatherdevelops.SBIs rarely occur under these weather conditions. However,before the passage of a cold front,the site is located within a warmer air mass,and winds are calm.The lower atmosphere is statically stable and SBIs can develop. The intensity of SBIs before a cold frontal passage is somewhat weaker than that under the anticyclonic pattern.

Table 1.Frequencies of occurrence of SBIs and NOIs and mean TIAI indices of SBIs for the different synoptic patterns.

Table 2.Mean depth,temperature difference across the inversion, and temperature gradient for SBIs.Values of SBI characteristics below(above)normal are marked by#(##).The signif i cance level of the F-test is 5%.

Table 3.As in Table 1,but for EIs.

Table 4.As in Table 2,but for EIs.

Tables 3 and 4 show the same information as Tables 1 and 2,but for EIs.EIs and SBIs have signif i cantly different characteristics,especially with regard to mean temperature differences and temperature gradients across inversions.The frequency of occurrence of prof i les with at least one inversion,the average number of inversions per prof i le,the mean depth,and the mean temperature difference across inversions are highest in magnitude for anticyclonic and CF patterns (shaded in black),and are lowest in magnitude for the AC andWP patterns.IntermediatevaluesareseenfortheCB pattern(shaded in gray).TIAI index values for EIs range from 1.38 to 5.46 for the WP and CF patterns,respectively.The difference in temperature gradients of EIs among all patterns is small(F-value=5.0),and the mean temperature gradient under the AC pattern is signif i cantly lower than that under the other f i ve synoptic conditions.The inversionstrength and activity of EIs are comparatively higher for anticyclonic patterns because these patterns are associated with air subsiding in the lower troposphere and gradually spreading over a wide area.Warming by adiabatic compression occurs and inversions are then formed.SBIs are destroyed by turbulence near the surface after sunrise,which leads to EIs separated from the ground.The strengthandactivityof EIs underthe CF pattern is the highest because cold front systems are associated with polar air masses movinginto lower latitudes.A polar air mass is colder than the air aloft,so creates strongerEIs.Convergenceand upwardmotionin the lower troposphereassociated with cyclonicconditionsdecreases static stability,which is not conducive to the formation of EIs.

The vertical distribution of inversion characteristics according to the synoptic pattern was examined.High vertical resolution radiosonde prof i les used in this work can provide detailed information about the vertical distribution of inversions in the lower atmosphere.Figure 2 shows the vertical distribution of daytime and nighttime frequencies of occurrence of inversions from the surface to 2 km for the six synoptic patterns.The distributions of nighttime inversion frequency for all synoptic patterns have a pronounced maximum from the ground to 100 m(Fig.2a).The rate of decrease with height is relatively higher above 100 m and inversion frequencies for all synoptic patterns are less than 5% beyond 1300 m.For AC and WP patterns,the frequency of inversions is greater than 70%and the rate of decrease with height is also signif i cantly higher below 500 m when compared to cyclonic and CF patterns.The vertical distributions of daytime inversion frequency for the six synoptic patterns are quite different from the nighttime cases(Fig.2b).There are almost no SBIs near the ground and inversion frequencies rapidly increase with height,reaching maximum values from300to 700m.Inversionfrequenciesforthe anticyclonic pattern peak between 300 m and 500 m.No clear maximum in inversion frequency below 2 km is seen for the CC and WP patterns.For the CF and CB patterns,inversion frequencies are higher and reach a maximum between 500 m and700 m.The effect of synoptic patterns on the vertical distribution of inversions is summarized as follows.Anticyclonic and WP patterns are associated with a stable low-level atmosphereand clearskies,which is conduciveto the formationof shallow and strong radiation inversions at night.Subsidence in the lower atmosphere is stronger for anticyclonic patterns, which leads to favorable conditions for the formation of EIs at night and during the day.Cyclonic patterns are associated with an unstable lower atmosphere and cloudy skies,which is not conduciveto the formationof SBIs and EIs duringboth daytime and nighttime.CF and CB patterns are associated with cold air invasions,which are favorable conditions for the development of EIs,but not of SBIs.

Overall,large-scale synoptic patterns strongly affect the activity,intensity,and vertical distribution of both SBIs and EIs.There is a signif i cant difference in the vertical distribution of daytime and nighttime inversions.Anticyclonic patternsareassociatedwithmaximainactivityandintensity,and cyclonic patterns are associated with minima in activity and intensity for both SBIs and EIs.The CF pattern is conducive to the development of EIs,but not to the formation of SBIs.

3.2.Effect of inversions on aerosol concentrations

The physical and chemical processes involved in the formation of aerosol CN are complex,especially during the day.Sources of mixed regional aerosols at the SGP CF may include sulfates from the oil ref i nery,power plant,and vehicular traff i c SO2emissions,smoke from local/regional agricultural burning,dust from desert regions, and the photochemically-driven production of organic or N-containing particles.Meteorological parameters such as wind,cloud,relative humidity,and precipitation strongly infl uence the formation and transportation of aerosols,and are dependent on the large-scale synoptic pattern.Under the samelarge-scalesynopticatmosphericcirculationconditions, especially at night,the effects of the above factors on the formation and transportation of aerosols are similar.Therefore, the following analysis is mainly focused on the accumulation of aerosol CN concentrations and its relationship with nighttime inversions for different large-scale synoptic patterns.

Table 5 shows the results of the one-way ANOVA and theLSD test for differences in the mean aerosol CN concentrationsinthepresenceofanSBI,anEI,andwhenthereis noinversion using 10 years of nighttime radiosonde pro fi les without considering synoptic patterns.At night,theF-statistic is 159.1,whichmeansthatmeanaerosolCN numberconcentrations are statistically correlated with the presence of an SBI at the 95%con fi dence level.The various LSD tests results show that there are signi fi cant differences in mean aerosol CN numberconcentrationsforthe SBI/NOI and SBI/EI cases (differenceinmeans,DM＜LSD).FortheEI/NOIcase,however,there is no signi fi cant difference(DM＞LSD).

Table 5.Results from the one-way ANOVA and the LSD test for differences in mean aerosol CN concentrations in the presence of an SBI,an EI,and when there is no inversion(NOI)during nighttime.

Table 6.Mean aerosol concentrations for all prof i les,all nighttime prof i les only,nighttime SBIs,nighttime prof i les without SBIs,and increasing rates in aerosol concentrations(in%)for the six synoptic patterns.Mean aerosol concentrations below(above)normal are marked by#(##).The signif i cance level of the F-test is 5%.

Table 6 summarizes the mean aerosol CN concentrations from all pro fi les,all nighttime pro fi les only,nighttime profi les containing SBIs only,nighttime pro fi les containing EIs only,and nighttime pro fi les without SBIs(including EI and NOI)for the six synoptic patterns.Statistical differences in aerosol CN concentrations among the synoptic patterns were also examined by applying one-way ANOVA and the LSD test.Differences in mean aerosol CN concentrations for the six synoptic patterns are statistically signi fi cant at the 95% con fi dence level,except when there are no SBIs(F-value= 4.1).AC and AE patterns have the highest values of mean aerosol CN concentrations(shaded in black),and cyclonic patterns(CC,CB,and WP)have the lowest values of mean aerosol CN concentrations.This difference is particularly signi fi cant for“all pro fi les”and“nighttime only”pro fi les(F-values are 28.1 and 23.2,respectively).Mean aerosol CN concentrations for AC and AE patterns are over 5000 cm−3, which is signi fi cantly higher than those for the other synoptic patterns.Mean aerosol CN concentrations during the night with SBIs,EIs,and without SBIs are similar,but the overall difference is smaller(F-values are 14.3,5.2,and 4.1,respectively).

Another important feature of the results presented in Table 6 is the impact of SBIs and EIs on aerosols for the six synoptic patterns.Aerosol CN concentrations in the presence of an SBI,an EI,and when there is no SBI are examined using the abovestatistical methodology.F-values for all categories are greater than 7.0 and LSD tests also show that there are signi fi cant differences in mean aerosol CN number concentrations for the SBI/no-SBI and SBI/EI classes.In order to quantitatively assess this impact,mean aerosol CN concentrations during the night with and without SBIs were compared.Mean aerosol CN concentrations when nighttime SBIs are present are signif i cantly greater than those without SBIs for all synoptic patterns,and the increase in percentage is different.For anticyclonic patterns,mean aerosol CN concentrations are the greatest whether an SBI occurred or not. Mean aerosol CN concentrations when an SBI is present are 23.7%and 29.8%higher than when there is no SBI for AC and AE patterns,respectively.For cyclonic patterns,mean aerosolCNconcentrationsarelowerwhetheranSBIoccurred or not.Percentage increases for CC,WP,and CB patterns are 16.1%,22.6%,and 24.5%,respectively.The mean aerosol CN concentrations for the CF pattern is 58.7%higher when an SBI is present than when there is no SBI.This is the highest percentage value calculated for all patterns.

The differences in aerosol mean CN concentrations for anticyclonic synoptic patterns can be attributed to the enhancement of static stability in the lower atmosphere under anticyclonic synoptic conditions,which constrains the diffusion of aerosols and favors the accumulation of aerosols near the ground.Cyclonic synoptic patterns associated with convergence and ascending motion near the ground are conducive to the vertical transport and diffusion of aerosols. TIAI index values and temperature gradients of SBIs for cyclonic synoptic patterns are relatively low,so the ability of SBIs to trap aerosols near the ground is weaker.For anticyclonic synoptic patterns,the activity and static stability of SBIs are relatively stronger,so SBIs can more effectively constrainthediffusionof aerosolsto nearthe ground.Theactivity and static stability of SBIs is strong before the passage of a cold front,and almost all SBIs occurredunder these conditions,so aerosol CN concentrations at the SGP CF were particularly high then.After the passage of a cold front, aerosol CN concentrations reduce rapidly,due to scavenging effects of the cold front on aerosols.This explains why mean aerosol CN concentrations when SBIs are present are much higher than when no SBI is present for the CF pattern.

Theanalysis of the effects ofSBIs onaerosol CN concentrations presented above demonstrates that the static stability in the lower atmosphere directly affects the accumulation of aerosols near the ground.Li(2012)conf i rmed that the temperature gradient is the most relevant of inversion parameters withregardtothenighttimesurfaceaccumulationofaerosols. A quantitative examination of the relationship between temperature gradient(rate of temperature increase across an SBI or from the surface to 300 m above)and aerosol CN concentrations at night for different synoptic patterns was done. Figure 3 shows that a positive linear relationship exists between temperature gradient and aerosol CN concentrations for all six synoptic patterns.The slopes of the linear equations represent the rate of change of aerosol CN concentrations with temperature gradient and the intercepts represent mean aerosol CN concentrations in a quasi-neutral lower atmosphere.This conf i rms that the temperature gradient directly affects the diffusion of aerosols near the ground.Mean aerosol CN concentrations in a quasi-neutral lower atmosphere and rates of aerosol change are different for different synoptic patterns.For the CC pattern,both the rate of aerosol change[281 cm−3°C−1(100 m)−1]and mean aerosol concentrations(3465cm−3)are low,so the effect ofthis synoptic atmospheric circulation pattern on aerosols is weak.For the WP,CB,and AC patterns,rates of aerosol change are almost the same[～400 cm−3°C−1(100 m)−1]and signif i cantly higher than seen when a CC pattern is present.Mean aerosol CN concentrations are different,so the ability of SBIs to trap aerosols near the ground is stronger.The rate of aerosol change under CF conditions[624 cm−3°C−1(100 m)−1]is the highest because the passage of a cold front dramatically changes the static stability in the lower atmosphere.Contrary to expectations,the rate of aerosol change for the AC pattern [141 cm−3°C−1(100 m)−1]is the lowest(R2=0.52).An explanation may be that this kind of synoptic pattern is associated with strong horizontal divergence and subsidence in the lower troposphere.This synoptic pattern is conducive to the accumulation of aerosols near the ground,so the effect of SBIs on aerosols is independent of temperature gradient.

The inf l uence of synopticpatterns on aerosol CN concentrations is strong.Anticyclonic patterns are associated with higher aerosol concentrations and cyclonic patterns are associated with lower aerosol concentrations.The presence of an SBI results in increased aerosol CN concentrations for all synoptic patterns,but the magnitude of the change in aerosol CN concentrations is different among the different synoptic patterns.The passage of a cold front has a strong scavenging effect on aerosols.There are signif i cant relationships between temperature gradient and aerosol CN concentrations under the same large-scale synoptic pattern,and these relationships are different for different synoptic patterns.This information may be useful for air pollution diffusion modeling.

4.Conclusions

Using the SOM weather classif i cation method,10 years of atmospheric prof i les and aerosol CN concentration data from the SGP CF and the sea level pressure product from the NARR were used to examine the statistical characteristics of inversionsand to quantify the impact of inversions on aerosol CN concentrationsfor different large-scale synoptic patterns. The main f i ndings are:

(1)The highest frequency,activity,and intensity of SBIs are associated with anticyclonic patterns.EIs occur most frequently and are more intense under anticyclonic and CF conditions.

(2)An examination of the vertical distributions of inversion frequency for the six synoptic patterns further conf i rms thatsynopticpatternsaffecttheactivityandintensityofinversions.The vertical distribution of inversions varies diurnally. At night,a peak in inversion frequency is seen between the groundand100m,andduringtheday,a peakis seen between 300 m and 700 m.

(3)Mean aerosol CN concentrations are highest for anti-cyclonic patterns and are lowest for cyclonic patterns.This is partly because anticyclonic conditions increase the static stability in the lower atmosphere and cyclonic conditions are associated with convergence and ascending air motion near the ground.During the night,SBIs are one of the key factors that inf l uence the accumulation of aerosols.The degree of change in aerosol CN concentrations is greater for anticyclonic patterns than for cyclonic patterns,and is the highest for the CF pattern.

(4)Thetemperaturegradientin theloweratmospherecorrelatesfairlywellwithaerosolconcentrationsneartheground for all synoptic patterns,but rates of aerosol concentration change with temperature gradient are signif i cantly different. The rate of aerosol concentrationchange is～400 cm−3°C−1(100 m)−1for the WP,CB and AC patterns,and～280 cm−3°C−1(100 m)−1for the CC pattern.Due to the strong scavenging effect of a cold front,the rate of aerosol concentration change is the highest[～620 cm−3°C(100 m)−1]under this synoptic condition.The lowest rate of aerosol concentration change[140 cm−3°C(100 m)−1]is found when an AC atmospheric circulation pattern is in place.

Overall,these results provide strong evidence regarding the behavior of inversions and their effects on aerosols for differentlarge-scalesynoptic patternsand providegeneralinsight into the relationship between temperature gradients in the lower atmosphere and aerosols near the ground.Quantitative assessments of inversion statistics,the impact of SBIs on aerosol CN concentrations,and the relationships between temperature gradients and aerosol concentrations are importantformodelingandpredictingthediffusionofaerosolsnear the ground under different synoptic conditions.A generalized additive model approach is an effective tool to develop air pollutant models based on historical pollutant concentrations,and meteorological and other relevant data.This approach has been used to model and predict daily air pollutants(Carslaw et al.,2007).Using the results of this study combined with the generalized additive model approach,we plan to develop aerosol diffusion models to more effectively assess relationships between inversions and aerosols,and to more accurately predict aerosol concentrations near the ground under different large-scale synoptic atmospheric circulation conditions.

Acknowledgements.The data used in this work were made available by the Atmospheric Radiation Measurement(ARM)Program sponsored by the U.S.Department of Energy(DOE).This work was supported by the Ministry of Science and Technology of China(Grant Nos.2010CB950804 and 2013CB955801),the“Strategic Priority Research Program”of the Chinese Academy of Sciences(Grant No.XDA05100300),and the National Natural Science Foundation of China(Grant No.41305011).

REFERENCES

Bailey,A.,T.N.Chase,J.J.Cassano,and D.Noone,2011:Changing temperature inversion characteristics in the US Southwest and relationships to large-scale atmospheric circulation. J.Appl.Meteor.Climatol.,50(6),1307–1323.

Carslaw,D.C.,S.D.Beevers,and J.E.Tate,2007:Modeling and assessing trends intraff i c-relatedemissions usingageneralized additive modeling approach.Atmos.Environ.,41(26), 5289–5299.

Cassano,J.J.,P.Uotila,and A.Lynch,2006:Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-f i rst centuries,Part1:Arctic.Inter.J.Climatol.,26(8), 1027–1049.

Cheng,C.S.Q.,and Coauthors,2007:A synoptic climatological approach to assess climatic impact on air quality in southcentral Canada.Part I:Historical analysis.Water Air,and Soil Pollution,182(1–4),131–148.

Dong,X.Q.,P.Minnis,and B.K.Xi,2005:A climatology of midlatitude continental clouds from the ARM SGP Central Facility:Part I:Low-level cloud microphysical,microphysical,and radiative properties.J.Climate,18(9),1391–1410.

Fedorovich,E.,P.Kaiser,M.Rau,and E.Plate,1996:Wind tunnel study of turbulent f l ow structure in the convective boundary layer capped by a temperature inversion.J.Atmos.Sci.,53(9), 1273–1289.

Fisher,R.A.,1966:The Design of Experiments.8th ed.,Hafner Publishing Company,New York,248 pp.

Hewitson,B.C.,and R.G.Crane,2002:Self-organizing maps: Applications to synoptic climatology.Climate Research, 22(1),13–26.

Holdridge,D.,J.Prell,M.Ritsche,and R.Coulter,2011:Balloonborne sounding system(BBSS)handbook.DOE ARM Tech. Rep.TR-029,11pp.[Available onlineathttp://www.arm.gov/ publications/tech reports/handbooks/sonde handbook.pdf?id =66.]

Holzworth,G.C.,1972:Vertical temperature structure during 1966 Thanksgiving week air-pollution episode in New York City.Mon.Wea.Rev.,100(6),445–450.

Huth,R.,C.Beck,A.Philipp,M.Demuzere,Z.Ustrnul,M. Cahynov,J.Kysely,and O.E.Tveito,2008:Classif i cations of atmospheric circulation patterns recent advances and applications.Trends and Directions in Climate Research,L.Gimeno,R.Garcia-Herrera,and R.M.Trigo,Eds.,Blackwell Publishing,Oxford,105–152.

Janhall,S.,K.F.G.Olofson,P.U.Andersson,J.B.C.Pettersson, and M.Hallquist,2006:Evolution of the urban aerosol during winter temperature inversion episodes.Atmos.Environ., 40(28),5355–5366.

Jefferson,A.,cited 2014:Aerosol observing system handbook. [Available online at http://www.arm.gov/Publications/tech reports/handbooks/aos handbook.pdf.]

Kahl,J.D.,1990:Characteristics of the low-level temperature inversion along the Alaskan Arctic coast.Inter.J.Climatol., 10(5),537–548.

Kohonen,T.,1998:Theself-organizing map.Neurocomputing,21, 1–6.

Li.J.,2012:Analysis of theinteractions between atmospheric temperature inversion and aerosols using ARM data.PhD Thesis, Key Laboratory of Middle Atmosphere and Global Environment Observation,Institute of Atmospheric Physics,Chinese Academy of Sciences,60–65.

Malek,E.,T.Davis,R.S.Martin,and P.J.Silva,2006:Meteorological and environmental aspects of one of the worst national air pollutionepisodes(January,2004)inLogan,CacheValley, Utah,USA.Atmospheric Research,79(2),108–122.

Milionis,A.E.,and T.D.Davies,1992:A 5-Year climatology ofelevatedinversions atHemsby(UK).Inter.J.Climatol.,12(2), 205–215.

Milionis,A.E.,and T.D.Davies,2008:The effect of the prevailing weather on the statistics of atmospheric temperature inversions.Inter.J.Climatol.,28(10),1385–1397.

Pearce,J.L.,J.Beringer,N.Nicholls,R.J.Hyndman,P. Uotila,and N.J.Tapper,2011:Investigating the inf l uence of synoptic-scale meteorology on air quality using selforganizing maps and generalized additive modeling.Atmos. Environ.,45(1),128–136.

Pope,C.A.,D.W.Dockery,X.Xu,F.E.Speizer,J.D.Spengler,and B.G.Ferris,1993:Mortality risks of air-pollution-a prospective cohort study.Am.Rev.Respir.Dis.,147(4),A13–A13.

Serreze,M.C.,J.D.Kahl,andR.C.Schnell,1992:Low-level temperature inversions of the Eurasian Arctic and comparisons with Soviet drifting station data.J.Climate,5(6),615–629.

Sheridan,P.J.,D.J.Delene,and J.A.Ogren,2001:Four years of continuous surface aerosol measurements from the Department of Energy’s Atmospheric Radiation Measurement Program Southern Great Plains Cloud and Radiation Testbedsite. J.Geophys.Res.,106(D18),20735–20747.

Wallace,J.,D.Corr,and P.Kanaroglou,2010:Topographic and spatial impacts of temperature inversions on air quality using mobile air pollution surveys.Science of the Total Environment,408(21),5086–5096.

Watanabe,O.,1998:Air Pollution Control Technology Manual. [Available online at https://www.env.go.jp/earth/coop/coop/ document/01-apctme/contents.html]

Wendisch,M.,S.Mertes,A.Ruggaber,and T.Nakajima,1996: Vertical prof i les of aerosol and radiation and the inf l uence of a temperature inversion:Measurements and radiative transfer calculations.J.Appl.Meteor.,35(10),1703–1715.

:Li,J.,H.B.Chen,Z.Q.Li,C.P.Wang,M.Cribb,and X.H.Fan:Low-level temperature inversions and their effect on aerosol condensation nuclei concentrations under different large-scale synoptic circulations.Adv.Atmos.Sci.,32(7), 898–908,

10.1007/s00376-014-4150-z.

(Received 5 July 2014;revised 23 October 2014;accepted 15 November 2014)

∗Corresponding author:CHEN Hongbin Email:chb@mail.iap.ac.cn

©Institute of Atmospheric Physics/Chinese Academy of Sciences,and Science Press and Springer-Verlag Berlin Heidelberg 2015