Analysis and Evaluation of the GlobalAerosolOpticalProperties Simulated by an Online Aerosol-coup led Non-hydrostatic Icosahedral Atmospheric M odel

DAITieSHIGuangyuand TeruyukiNAKAJIMA

1State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute ofAtmospheric Physics,Chinese Academy of Sciences,Beijing100029

2Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology,Nanjing210044

3Collaborative Innovation Center on Forecastand Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology,Nanjing210044

4Atmosphere and Ocean Research Institute,University of Tokyo,Kashiwa,Japan

Analysis and Evaluation of the GlobalAerosolOpticalProperties Simulated by an Online Aerosol-coup led Non-hydrostatic Icosahedral Atmospheric M odel

DAITie∗1,2,3,SHIGuangyu1,3,and TeruyukiNAKAJIMA4

1State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute ofAtmospheric Physics,Chinese Academy of Sciences,Beijing100029

2Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology,Nanjing210044

3Collaborative Innovation Center on Forecastand Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology,Nanjing210044

4Atmosphere and Ocean Research Institute,University of Tokyo,Kashiwa,Japan

Aerosolopticalproperties are simulated using the SpectralRadiation TransportModel for AerosolSpecies(SPRINTARS) coupled w ith the Non-hydrostatic ICosahedralAtmospheric Model(NICAM).The 3-yearglobalmean all-sky aerosoloptical thickness(AOT)at550 nm,the˚Angstr¨om Exponent(AE)based on AOTs at440 and 870 nm,and the single scattering albedo (SSA)at 550 nm are estimated at 0.123,0.657 and 0.944,respectively.For each aerosol species,the mean AOT is w ithin the range of the AeroCom models.Both the modeled all-sky and clear-sky results are compared w ith observations from the Moderate Resolution Imaging Spectroradiometer(MODIS)and the Aerosol Robotic Network(AERONET).The simulated spatiotemporal distributions of all-sky AOTs can generally reproduce the MODIS retrievals,and the correlation and model skill can be slightly improved using the clear-sky results over most land regions.The differences between clear-sky and all-sky AOTs are larger over polluted regions.Compared w ith observations from AERONET,the modeled and observed all-sky AOTs and AEs are generally in reasonable agreement,whereas the SSA variation is notwell captured.A lthough the spatiotemporal distributions of all-sky and clear-sky results are similar,the clear-sky results are generally better correlated w ith the observations.The clear-sky AOT and SSA are generally lower than the all-sky results,especially in those regions where the aerosol chem ical composition is contributed to mostly by sulfate aerosol.The modeled clear-sky AE is larger than the all-sky AE over those regions dominated by hydrophilic aerosol,while the opposite is found over regions dominated by hydrophobic aerosol.

aerosolopticalproperties,non-hydrostatic icosahedralatmospheric model,Moderate Resolution Imaging Spectroradiometer,Aerosol Robotic Network

1.Introduction

Atmospheric aerosols have great impacts on the environment,human health,and the earth’s climate(Twomey,1974; Kampa and Castanas,2008;Zhang etal.,2012a).Currently, the effects of aerosol on climate(especially the interactions among aerosols,radiation,and clouds)are one of the largest uncertaintiesin modelsimulationsand climate change assessment(Lohmann et al.,2010).To properly quantify aerosol effects on the climate system,we need to accurately estimate aerosol optical properties such as aerosol optical thickness (AOT),˚Angstr¨om exponent(AE)and single scattering albedo (SSA)w ith models(Goto etal.,2012).

The opticalproperties ofaerosols are determ ined notonly by the aerosol amount,but also by physical and optical parameters such as aerosolsize distribution,the mixing state of particles,hygroscopic grow th,and refractive indices,especially in absorbing particles such as black carbon(BC)and dust.These parameters are either prescribed empirically or calculated explicitly in global climate aerosolmodels(Kinne etal.,2006;Textor etal.,2007;Peng etal.,2012;Zhang etal.,2012b;Mann etal.,2014),and the uncertainties of such parameterscan induce significantdifferencesin the simulated aerosolopticalproperties(Goto etal.,2011b).Aerosolmodeling also suffers from poorly known aerosol life cycles and em ission inventories(Textor et al.,2006,2007).Thus,the aerosol model has to be evaluated against observations before we can place confidence in such a model(Takemura et al.,2002a;Prados etal.,2007;Chin etal.,2009;Su and Toon, 2011;Ridley etal.,2012).

Evaluations of simulated aerosoloptical fields in climate models have been performed in many previous studies,and resulting know ledge of aerosol processes has generally improved(Chin etal.,2002;Kinne etal.,2003;Lee and Adams, 2010;Chin et al.,2014).This makes modeled AOTs generally comparable to observations.However,the pathway to making such a match is less well constrained,and uncertainties associated w ith aerosol and aerosol–cloud interaction modeling are still large(Textor et al.,2006;Lee et al.,2013).Model results and observations are often compared in an inconsistent manner(Chin et al.,2002).Observations of aerosol optical properties are generally retrieved only under cloud-free conditions,whereasmodel results used for comparison are generally calculated under all-sky conditions.The effect of such an inconsistent comparison on modeled AOT evaluation has been studied recently(Colarco et al.,2010).The indication is that sampling model output consistently w ith satellite AOT retrievals is a more appropriate methodology to making aerosolmodelevaluations. Separating the modeled aerosol optical properties w ith a new aerosol-coupled version of the Non-hydrostatic ICosahedralAtmospheric Model(NICAM)into all-sky and clear-sky properties,we presenta model to observation comparison of the AOT,AE and SSA using both the modeled all-sky and clear-sky results in the presentstudy.In the nextsection,the model setup and observation data used are described.The general model performances are shown in section 3.1.The model results are further evaluated by comparing them w ith MODIS and AERONET retrievals in sections 3.2 and 3.3,respectively.The paper closes w ith a conclusion in section 4.

2.M odeldescription and observation data

2.1.Model description

The Non-hydrostatic ICosahedral Atmospheric Model (NICAM)isdesigned to perform cloud-resolving simulations by directly calculating deep convection and mesoscale circulations,which play key roles not only in tropical circulation but also the global circulation of the atmosphere(Satoh et al.,2008).The model has been used for several types of global cloud-resolving experiments w ith horizontal resolutions up to 3.5 km(Satoh et al.,2008),including a realistic simulation of the Madden–Julian Oscillation(M iura et al.,2007).These studies demonstrate that NICAM reproduces the detailed features of global cloud and precipitation fields.The Spectral Radiation Transport Model for Aerosol Species(SPRINTARS)is a global three-dimensionalaerosol transport–radiation model,described fully in Takemura etal. (2000;2002a;2009)and Goto etal.(2011a).In the aerosolcoupled version of NICAM(Suzuki et al.,2008),which is referred to as NICAM+SPRINTARS,the mass m ixing ratios of the main tropospheric aerosols,i.e.,carbonaceous aerosols (BC and organic carbon),sulfate,soil dust,sea salt,and the precursor gases of sulfate,are predicted w ith the transport processes including advection,convection,diffusion,gravitational settling,and wet and dry deposition.The advection scheme of NICAM has desirable requirements for tracer transportsimulations:mass conservation,monotonicity,and efficiency(Niwa etal.,2011b).These facts encourage us to use NICAM+SPRINTARS as an aerosol transportmodel.

The present study requires a long-term model integration to include the aerosol seasonal variation.This makes it too expensive to perform the modelsimulation w ith cloudresolving resolutions,which directly simulate the cloud microphysics using a one-moment(Tomita,2008)or twomoment bulk scheme(Seiki and Nakajima,2014;Seiki et al.,2014)for several days only(M iura et al.,2007;Suzuki et al.,2008). NICAM can also be run at coarser resolutions(Niwa et al.,2011a;Dai et al.,2014a),using the prognostic Arakawa–Schubert cumulus convection scheme (Arakawa and Schubert,1974)and large-scale condensation scheme(Le Trent and Li,1991)for cloud parameterization.NICAM is still advantageous when run at coarser resolutions,especially for transport simulations,because of the conservation of mass.Thus,we perform the model simulation w ith a coarse horizontal resolution of 224 km and 40 vertical layers and the model top located at 40 km for four years(2005–08).The fi rst year is used for spinup.The column cloud fraction is calculated w ith the commonly used maximum-random overlap method(Geleyn and Hollingsworth,1979).The other model physics,such as the m inimal advanced treatments of surface interaction and runoff(MATSIRO)land surface scheme(Takata etal.,2003), the two-streamk-distribution radiation scheme(Nakajima et al.,2000;Sekiguchiand Nakajima,2008),and the level2 vertical turbulence closure scheme(Mellor and Yamada,1974) are identical to those used in the cloud-resolving resolutions. NationalCenters for EnvironmentalPrediction(NCEP)Final (FNL)operational global tropospheric analyses are used for the initialand boundary conditions.

The emission inventories of BC are based on the Global Em issions Inventory Activity(GEIA)database(Cooke and Wilson,1996),as monthly means w ithout yearly variation, w ith the exception of the fossil fuel consumption em ission. The latter is based on yearly mean data taken from the Aero-Com phase-IIdataset(Diehletal.,2012).Assuming different em ission ratios of OC to BC according to the burning conditions(Takemura etal.,2000;Takemura etal.,2002a),the OC em ission flux is calculated by the model itself.The inventory of sulfate aerosolprecursor(SO2)is also taken from the AeroCom phase-IIdataset.The dustand sea saltem ission fluxes are parameterized as in Takemura etal.(2009).The oxidant concentrations,such as ozone and hydroxyl radical,which are required to calculate sulfate chem istry(Takemura et al.,2000),are given by a global chemical transportmodel(Sudo et al.,2002).For proper simulation of the aerosol distribution,the modeled w ind,water vapor,and temperature fields are nudged to the NCEP FNL analysis data w ith a time-scale of six hours.

The modeled AOT,AE and SSA are calculated in the same way as Daietal.(2014a)by using the new ly proposed opticalparameters.Hygroscopic grow ths for sulfate,organic carbon,and sea salt are parameterized as a function of relative hum idity to consider the aerosol water uptake(Takemura etal.,2002a).The relative humidity iscalculated identically forclear-sky and cloud-sky gridsbased on the Clausius–Clapeyron equation w ith grid mean values(i.e.,grid mean specific hum idity and temperature).The model integral time step is 20 m inutes,and the aerosoloptical properties are calculated at each integral time step but archived at every 3 hours.The modeled daily mean aerosolopticalpropertiesare simple means of the eight instantaneous snapshots per day. To exam ine the effectof cloud on the evaluation of the model results,we separate the simulated monthly mean aerosolopticalproperties into clear-sky and all-sky properties.We sample the modeled daily aerosoloptical properties to the daily cloud-free observations and calculate the modeled clear-sky monthly aerosoloptical properties by averaging the sampled daily results,whereas the all-sky ones are calculated w ithout any conditionalsampling.

2.2.The MODIS products

The Moderate Resolution Imaging Spectroradiometer (MODIS)is a key instrument onboard the NASA earth observing system satellites(Salomonson et al.,1989;Barnes et al.,1998).It has the ability to monitor the spatiotemporal variation of the globalaerosoland cloud fields over both ocean and land w ith severalwell-calibrated spectralchannels (King etal.,1992;Kaufman etal.,1997;Tanre´ etal.,1997). To elim inate the strong solar reflectance by cloud,MODIS Level 2 AOT retrieval at a 10×10 km2resolution considersonly the bestcloud-free pixelsusing a sophisticated cloud screen as a preprocessing step(Ackerman etal.,1998;Martins etal.,2002;Remer etal.,2005).The Level2 AOT is furtheraggregated to Level3 gridded globalproductata 1◦×1◦resolution(King etal.,2003;Remer and Kaufman,2006).In the present study,the MODIS Collection 5.1 daily Level 3 products of AOT at550 nm and cloud fraction from both the Terra and Aqua satellites are used,which can be downloaded freely from NASA’s innovative data analysis and visualization system(http://disc.sci.gsfc.nasa.gov/giovanni/overview/ index.htm l)(Ackerand Leptoukh,2007).

2.3.AERONET dataset

The Aerosol Robotic Network(AERONET)provides the largest dataset of global aerosol optical properties derived from ground-based remote sensing using sun/skyradiometers(Holben etal.,1998;Dubovik etal.,2000).In the presentstudy,the daily average AERONET Level2.0 almucantar inversion products are used for comparison(http:// aeronet.gsfc.nasa.gov/cgi-bin/combined data access inv). The AERONET AOTs and SSAs at both 440 and 675 nm are interpolated to compare w ith the modeled results at550 nm under the assumption that the AOTs are proportional to wavelength on a logarithm ic scale.The AE used for comparison is determ ined from the AOTs at440 and 870 nm.

3.Results

3.1.Global aerosol distribution with NICAM

Figure 1 shows the three-year averaged global distribution of simulated AOT under all-sky conditions at the wavelength of 550 nm for individual aerosol components and its relative contribution to the total AOT.The sulfate and dust aerosols are located mainly in the Northern Hem isphere, whereas the carbonaceous aerosols and sea salt are located mainly in the Southern Hem isphere.High AOT values(＞0.2) for sulfate aerosolare found in eastern Asia and Europe because of the high em ission of the sulfate aerosol precursor SO2from fossil fuel consumption.Carbonaceous aerosols originating from biomass burning are prom inent in central and southern A frica,Southeast Asia,and South America, w ith AOT values generally higher than 0.2.The maximum value of dust AOT(＞0.3)is seen over the Sahara Desert area,and the dustw ith high AOT em itted from the deserts of East Asia is also simulated well(Wang etal.,2008;Bietal., 2011).High sea salt AOT(＞0.1)located near 60◦S directly reflects the high em ission rates due to the strong surface w ind. In terms of the global3-yearmeans,soildustaerosolhas the largestAOT(0.035),followed by sulfate aerosol(0.032),carbonaceous aerosol(0.030),and sea salt(0.026).As shown in Table 1,the mean AOTs of NICAM for both aerosol species and the totalare allw ithin the ranges of the 20 aerosolmodels that participated in the AeroCom exercise(Kinne et al., 2006).For dust aerosol,sulfate aerosol and the total,the mean AOTs are close to(～10%)the AeroCom means.For carbonaceous aerosoland sea salt,the mean AOTs are 30.4% higher and 18.7%lower than the AeroCom means,respectively.Sulfate aerosol usually contributes more than 40% to the total AOT over major pollution regions,such as East Asia,Europe,and eastern America.On the other hand,carbonaceous aerosols contribute most(＞60%)to the totalAOT overbiomass burning regions.Sea saltaerosolcontributes the most to the AOT over oceans,except the paths of the Asian aerosol transpacific transportand the Sahara dusttransatlantic transport.Dustaerosolcontributesover60%to the totalAOT over the desertsource and outfl ow regions.

AE indicates the wavelength dependence of AOT,which is used commonly to infer the aerosol particle size distribution and chem ical composition(Chung et al.,2012;Logan et al.,2013).Small aerosol particles(i.e.sulfate and carbonaceous)have strong wavelength dependence and thus large AE.SSA governs the strength of aerosol in absorption(Dubovik et al.,2000).The AE and SSA both have spatial distributions related to the aerosol chemical composition(figure not shown for brevity).Large AEs(＞1.0)in biomass burning and pollution regions are found becausesmall aerosol particles(sulfate and carbonaceous)are dominant in such areas.Small AEs(＜0.6)are seen in the dust or sea salt aerosol predominant regions because the aerosol particles are large.Dustand carbonaceous aerosols make the SSAs as smallas 0.86–0.90 because of theirstrong absorption properties.Over the remote ocean,especially in the Southern Hem isphere,the SSAs are around 1.0,as non-absorbing sea saltaerosoldominates.

Fig.1.NICAM-simulated 3-yearaveraged all-sky AOTs at550 nm(leftcolumn)for individualaerosolcomponents and its contribution to the total AOT(rightcolumn).

Table 1.Globally and annually averaged AOTs at550 nm w ith NICAM+SPRINTARS,the AeroCom means,and the AeroCom ranges.

3.2.Comparisons with MODIS retrievals

The modeled climatology of all-sky AOTs at550 nm,the corresponding MODIS retrievals,and the discrepancies for January,April,July,and October are shown in Fig.2.The simulated AOTs can reproduce the general characteristics of aerosol distribution as observed by MODIS.AOTs are commonly higher over the Saharan,Arabian and East Asian regions,and the seasonal variation of AOT w ith higher values in Apriland July ismostly regulated by the largerdustaerosol em issions(Yang etal.,2008;Ridley etal.,2012).Although the model tends to overestimate the AOTs overbiomass burning regions in July,the strong seasonal cycles of the biomass burning in the Congo and Amazon basins are captured.The transpacific transportof the aerosolplume from EastAsia to North America(Takemura etal.,2002b;Logan etal.,2010) is evident from both the model and satellite results.The discrepancies reveal the model tends to underestimate the transatlantic transport of the Saharan Desert dust and overestimate the transpacific transportof the EastAsian aerosols, exceptduring the summerseason.

To investigate the effectof cloud cover on AOT simulation,the modeled climatology of all-sky and clear-sky AOTs are compared in Fig.3.Distinct differences are found over the regionsof EastAsia,Europe,and eastern America,where aerosols are mostly from pollution sources.The clear-sky AOTs are generally lower than the all-sky AOTs,especially in January.The maximum absolute and relative differences over-0.3 and-30%,respectively,are found over eastern China in January.To clarify the reason for such maximal differences,the modeled and MODIS-retrieved cloud fraction are also compared.The MODIS cloud fraction is highest over eastern China in January(figure notshown for brevity), and this w illcause more highermodeled AOTs to be masked out for the climatology of clear-sky AOTs because the sulfate aerosol is mostly formed in clouds and the hygroscopic grow th is more effective in higher hum idity regions near the clouds(Takemura et al.,2000;Goto et al.,2011a).Meanwhile,we find there is a clear correlation between the simulated cloud fraction distributions and MODIS results,although the model tends to underestimate the cloud fraction over North America,Eurasia,and the western coasts of the main continents,as in many other models(Le Trent and Li, 1991).Detailed verification of the modeled cloud structures is beyond the scope of this study.Over the tropical and subtropicalocean regions,the clear-sky AOTs are generallyslightly higher(＜0.05)than the all-sky ones,and this could induce some high relative differences where the AOTs are also small,such as over the tropical Pacific.The clear-sky AOTs are generally slightly lower(＜-0.05)than the all-sky AOTs over the Southern Ocean near 60◦S,where the cloud fraction and sea salt aerosol are generally higher.This indicates that the sea salt AOT enhancement by hygroscopic grow th is larger than the decrement caused by the wet deposition under high-cloud or high-humidity conditions,and the large AOT under high cloud fraction conditions may be masked out to calculate the climatology of clear-sky AOT.

Fig.2.Modeled all-sky AOTs at 550 nm(left column),the corresponding MODIS-retrieved AOTs(m iddle column),and the differences between the modeled and MODIS-retrieved AOTs(right column)in January(top row),April(2nd row),July(3rd row),and October(bottom row)averaged over the 3-yearperiod from 2006 to 2008.

Fig.3.Absolute(leftcolumn)and relative(rightcolumn)differences of the modeled all-sky and clear-sky AOTs in January(top row),April(2nd row),July(3rd row),and October(bottom row)averaged over the 3-yearperiod from 2006 to 2008.The absolute difference is defined as the clear-sky AOT m inus the all-sky AOT.The relative difference is defined as the ratio of the clear-sky AOT to the all-sky AOT.

To evaluate the evolution of the modeled AOTs quantitatively,we compare the modeled AOTs over land and over ocean w ith MODIS retrievalsseparately.The global land area is divided into seven regions according to the aerosolsources and theirgeographical locations,sim ilar to Chin etal.(2009): North America(NAM),Europe(EUR),Asia(ASA),northern Africa and the M iddle East(NAF),South America(SAM), southern A frica(SAF),and Australia/New Zealand/tropical western Pacific countries(AUS)(Fig.4a).Figures 4b–ishow comparisons of the regional and global monthly mean modeled AOT over land under both clear-sky and all-sky conditions w ith the MODIS retrievals.The statistical parameters, including the correlation coefficient(R),bias,and modelskill are given in Table 2.The model skill depends on bothRand the standard deviations of the observed and modeled results:

whereσfis the ratio of the standard deviation of the model tothatof the observation,andR0is the maximum attainableR, which is set to 1(Taylor,2001;Chin etal.,2009).Note that the regionalmonthly mean AOT under all-sky conditions is calculated using only the grid values where monthly mean MODIS AOTs are available.It is clear that the modeled all-sky and clear-sky AOTs can both reproduce the monthly AOT variability as observed by MODIS,except over NAM where the modeled AOT variability is too small compared to MODIS results.Although the model tends to underestimate the AOTs over all regions,and the clear-sky AOTs further enlarge the underestimations,the clear-sky AOTs are better correlated w ith the observed AOTs.This indicates that the aerosol variations are better simulated using the modeled clear-sky results,and this is further verified by the increments of the model skill using the clear-sky AOTs over all regions.The globalocean is also divided into seven regions, as shown in Fig.5a:northern Atlantic(NA),northern Pacific(NP),tropical northern Atlantic(TA),southern Pacific (SP),southern Atlantic(SA),Indian Ocean(IO),and Southern Ocean(SO).Sim ilar comparisons over the ocean as over land are shown in Figs.5b–i,and the statistical parameters are given in Table 3.The modelshowshigherskillin simulating the monthly AOT variations over the downw ind regions of the main land aerosolsources,such as the outflows of dust aerosol from the Sahara Desert(TA),m ixed aerosols from East Asia(NP),and biomass burning aerosols from South America(SA).The differences between clear-sky and all-sky AOTs are generally ignorable,except over the NA and SOregions.A lthough theRvalues of the clear-sky AOTs w ith observations are higher over the SO region,the increment of underestimation using clear-sky AOTs further induces the decrement of the model skill.On the basis of the global ocean,the clear-sky AOT is better correlated w ith the observation and slightly increases the modelskill.

Table 2.Summary of the statisticalparameters for the comparisons shown in Fig.4.

Fig.4.(a)Definition of the different land regions used in this study.The surrounding panels compare the variation ofmodeled monthly all-sky AOT(red line),clear-sky AOT(green line),and MODIS AOT(black line)over(b–h)the different regions and (i)the global land area.

Table 3.Summary of the statisticalparameters for the comparisons shown in Fig.5.

Fig.5.(a)Definition of the differentocean regions used in thisstudy.The surrounding panelsshow comparisonsof the variation of modeled monthly all-sky AOT(red line),clear-sky AOT(green line),and MODIS AOT(black line)over(b–h)the different regions and(i)the globalocean.

Figure 6a shows the 3-year mean differences of the modeled daily AOTs and the MODIS retrievals over land as a function of MODIS cloud fraction.During the comparison, the modeled AOTs are sampled to the observations for the regionalmean.The differences are clearly dependent on the cloud fraction over all regions.The model tends to overestimate the AOTs over the AUS,SAF,SAM,and NAF regions under low cloud fraction conditions,whereas underestimations are found underhigh cloud fraction conditions.In these regions,aerosols are mostly from dust and biomass burning sources.The cloud only affects the wet deposition of these aerosols.Insufficient wet deposition under low cloud fraction conditions induces the overestimation of the AOT.The model underestimates the AOTs under all cloud conditions over the ASA,EUR,and NAM regions.In these regions, aerosols are dom inated by sulfate.The cloud affects both the formation and the wet deposition of sulfate.A lthough the wet deposition is smallwhen the cloud cover is low,the insufficient formation of sulfate could cause the underestimation of AOT.It is interesting that the AOT underestimation increases w ith the cloud fraction over all regions.As shown in Fig.6b,the MODIS AOT increases w ith the cloud fractionoverall regions,and such enhancementcan be wellexplained as the aerosolhygroscopic grow th in the humid environment surrounding clouds(Chand et al.,2012).As shown in Fig. 6c,the model can generally reproduce AOT enhancements w ith the cloud fraction except over the NAF region;however,the slopes of enhancements are much smaller than in the MODIS retrievals.This indicates that the modelmay underestimate the effectof aerosol hygroscopic grow th,while MODIS may overestimate the AOT underhigher cloud fraction caused by the unscreened cloud particles w ith AOT uncertainties of about 5%–15%(Remer et al.,2005).In our model,the consideration ofhydrophobic dustaerosolinduces the decrementof the AOT w ith cloud fraction over the NAF region.Sim ilar results are also found over the ocean regions, as shown in Figs.6d–f.

Fig.6.Mean differences of the modeled and MODIS-retrieved daily AOTs over the 3-year period for varying cloud fraction over(a)land and(d)ocean,the mean MODIS-retrieved AOTs for varying cloud fraction over(b)land and(e) ocean,and the mean modeled AOTs w ith varying cloud fraction over(c)land and(f)ocean.

3.3.Comparisons with AERONET observations

AERONET simultaneously retrieves AOT,AE,and SSA (Dubovik and King,2000;Dubovik et al.,2000),and this makes more aerosol characteristics available to constrain modelperformances.The modeled monthly and 3-yearmean AOT and SSA at 550 nm and the AE based on AOTs at440 and 870 nm are compared w ith the AERONET retrievals. The monthly and 3-year mean AERONET-retrieved optical values are derived from the daily mean values.There are in total 148 AERONET sites thathave more than 120 daily mean retrievals during the period 2006 to 2008.The locations of these AERONET sites are shown in Fig.7a,and the AERONET sites are also further classified into seven world regions.The regional mean observed aerosol optical properties are calculated using the available observations at the AERONET sites located over each region,and the regional mean modeled results are calculated sim ilarly to the observed ones by interpolating the model results to the corresponding AREONET sites.

Figures 7b–ishow inter-comparisons of the modeled allsky,clear-sky,and the retrieved monthly mean AOT variations on the basis of regionaland global means,and the statisticalparameters are given in Table 4.Sim ilar comparisons for the AE and SSA are shown in Figs.8 and 9,and the statistical parameters are summarized in Tables 5 and 6,respectively.Over the NAM and EUR regions,where aerosols are mostly from pollution sources,the observed AOTs show clear seasonal variation,which is reproduced better by using the clear-sky AOTs than the all-sky AOTs,although the clear-sky AOTs are more biased than the all-sky AOTs over the NAM region.Such an influence is not so obvious w ith respect to the comparisons of the AE and SSA values.The monthly variations of AE and SSA are not clear,except that the AEs are slightly lower during the spring season in the NAM region.The latter could be caused by the frequent occurrence of Asian dust transpacific transport in the spring season(Logan et al.,2010).Over the biomass burning regions of SAM and SAF,the observed monthly variations of AOTs and AEs w ith peaks during biomass burning periods are also slightly improved w ith higher model skill by usingclear-sky results.The retrieved SSAs w ith lower values during the biomass burning season over the SAF region are also better simulated by the clear-sky results,although the bias is slightly higher than based on the all-sky results.Over the NAF and AUS regions,where aerosols are mostly from dust sources,except those perturbed by biomass burning,the observed AOT,AE,and SSA variations are also better reproduced by the clear-sky results w ith higherR.In the ASA region,where the aerosol composition is more complicated, although the biases of AOT,AE and SSA are enlarged w ith the clear-sky results,the variations are better reproduced by the clear-sky results w ith higherRand model skill for both AOT and AE.

Table 4.Summary of the statisticalparameters for the comparisons shown in Fig.7.

Fig.7.(a)Locations of the AERONET sites used in this study and the seven regions these sites are furtherdivided into.the surrounding panels show inter-comparisons between the modeled all-sky(red line),clear-sky(green line)and AERONET-retrieved (black line)monthly mean AOT variations at550 nm on the basis of the(b–h)regionalmean and(i)globalmean.

Fig.8.As in Fig.7 but for AEs(440/870 nm).

Fig.9.As in Fig.7 but for SSA at550 nm.

Table 5.Summary of the statisticalparameters for the comparisons shown in Fig.8.

Table 6.Summary of the statisticalparameters for the comparisons as shown in Fig.9.

Figure 10 shows an inter-comparison of the modeled allsky,clear-sky,and observed 3-year mean of AOT,AE and SSA over all the available AERONET sites.As shown in Figs.10a–c,on a global basis,the all-sky and clear-sky AOTs,AEs and SSAs are significantly correlated w ithRvalues of 0.963,0.985,and 0.950,respectively,indicating similar horizontal distributions of clear-sky and all-sky results.The 3-year mean all-sky AOT,AE,and SSA are 0.203, 0.895,and 0.916,respectively,which are 0.022,0.019,and 0.008 higher than the clear-sky values.The clear-sky AOT is generally lower than the all-sky AOT,except over the dustdom inant regions.The clear-sky AE is generally larger than the all-sky AE when the AE value is high(＞1.1),whereas the clear-sky AE is lower than the all-sky AE when the AE value is low(＜0.6).The high AE value indicates that the AOT is contributed to mostly by the sulfate and/or carbonaceous aerosols.These aerosol radiiare larger under cloudy conditions because of hygroscopic grow th,so the all-sky AE values are lower.In contrast,the low AEs indicate the aerosol composition is mostly dust.This hydrophobic aerosol is not influenced much by the cloud,but the extinction coefficients of hydrophilic aerosols in clear-sky conditions are lower than in all-sky conditions,and this induces the lower all-sky AEs.The clear-sky SSAs are mostly lower than the all-sky SSAs,especially over those regions dom inated by sulfate aerosol,further indicating the lesser contribution of the non-absorption sulfate aerosolunderclear-sky conditions. Figures 10d–i show comparisons between the modeled and AERONET-retrieved values,and the statistics that reveal the comparison between the model simulations and AERONET observations are summarized in a Taylor diagram(Taylor, 2001)(not shown for brevity).Comparing the modeled allsky AOTs w ith the AERONET retrievals,we find that they are generally in reasonable agreement w ithRranging from 0.419 in the EUR region to 0.921 in the AUS region,and the modeled standard deviations are generally lower than those of the retrievals.The latter could be induced by the coarse model resolution.The modeled value represents an average over a GCM grid box of about 220×220 km2,which is little affected by the local aerosol sources.The observations of AERONET may be influenced by the local aerosol sources,such as over the urban sites.This is further verifi ed by the general underestimation of high AOTs(＞0.5).Using the clear-sky AOTs,theRand the root-mean-square error are generally improved,except over the ASA region.Comparing the modeled all-sky AEs w ith the AERONET retrievals, we find that the variations of the AE values are captured well w ithR＞0.6 exceptover the NAF and AUS regions,while the model tends to underestimate the AE values over the NAM, EUR,and ASA regions.We consider two possible explanations for the AE underestimation here.One is that the removal processes of dust aerosols may be underestimated in our model,and this induces more suspended dust over the outflow regions.The lifetime or residence time of dust is 8.2 days in ourmodel,which is about tw ice thatof the AeroCom mean(4.2 days).The other possible explanation is thatour modelmay also have less scavenging for large dustparticles, and this induces an incorrect dust size distribution over the outflow regions.SPRINTARS uses a single-momentscheme to track only the dust mass in 10 bins,as compared to the two-momentdustmodel thatalso includes the size distribution(Adams and Seinfeld,2002;Peng etal.,2012).A lthough the underestimation of AE is further enlarged when using the clear-sky results,especially over the NAM and ASA regions,Ris generally improved.TheRvalues between modeled allsky SSAs and retrievals are generally low(＜0.3)and the modeled standard deviations are generally lower than those of the retrievals.The clear-sky values can slightly improve the value ofR.

4.Conclusion

The globalspatialand temporaldistributionsof the major aerosoloptical properties,i.e.,AOT,AE,and SSA,are simulated using a new aerosol-coupled non-hydrostatic icosahedral atmospheric model from 2006 to 2008.The 3-year global mean AOT,AE and SSA at 550 nm are estimated at 0.123,0.657 and 0.944,respectively,w ith soil dust having the largestAOT(0.035),followed by sulfate aerosol(0.032), carbonaceousaerosol(0.030),and sea salt(0.026).Forall the aerosol species,the mean AOTs are w ithin the ranges of the AeroCom results.

Fig.10.Inter-comparison of the modeled all-sky,clear-sky and AERONET-retrieved 3-year mean AOTs at 550 nm(left column),AEs(440/870 nm)(middle column),and SSAs at 550 nm(right column)at all the available AERONET sites in this study.Each point represents the site-specific 3-yearmean results,and points are colored according to the seven world regions.

To include the effectof cloud on the aerosolmodelevaluation,the model results are separated to all-sky and clear-sky results.The simulated spatial distribution of all-sky AOTs can generally reproduce the MODIS retrievals.The transpacific transport of the aerosol plume and the seasonal variation of AOTs are in reasonable agreementw ith the retrievals. Although the clear-sky results show larger bias to the observations,they are in better agreement w ith the retrievals w ith higherRand model skill.The differences between the modeled AOTs and observations are larger under the higher cloud fraction conditions.Compared w ith the ground-based AERONET observations,the modeled clear-sky AOT,AE, and SSA are generally in better agreementw ith observations than the all-sky results,based onR.The clear-sky AOTs and SSAs are generally lower than the all-sky results,especially over those regions where aerosols are mostly from pollution sources,because the non-absorbing sulfate is mostly formed in cloud and the hygroscopic grow th is more effective in higher hum idity regions near the cloud.The modeled clear-sky AEs could be either larger or smaller than the all-sky AEs,depending on the aerosolchemicalcomposition. Although largerdifferencesbetween all-sky and clear-sky results are found over the pollution regions,the differences are smaller than the aerosolseasonaland spatialvariations.

The modeled AEs are exclusively lower than the AERONET retrievals in the NAM,EUR,and ASA regions,highlighting the uncertainties of the aerosol processes in our model.An investigation of the model’s uncertainties using updated em ission inventories and observations(Levy et al.,2013)w ill provide multi-dimensional diagnostics of the model’s shortcomings,as well as possible remedies.Recently,the aerosol assimilation system of NICAM+SPRINTARS has been developed to overcome some of the uncertainties involved in the aerosol processes (Dai et al.,2013;Dai et al.,2014b),helping to improve thesimulation ofaerosolopticalproperties over EastAsia.

Acknow ledgements.SHIGuangyu and DAITie are supported by projects from National Natural Science Funds of China(Grant Nos.41130104,and 41475031),Open Research Program of Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration from Nanjing University of Information Science and Technology(Grant No.KDW 1302),the Public Meteorology Special Foundation of MOST(Grant No.GYHY201406023),the National Key Basic Research and Development Program(973 Program,2011CB403401),and Teruyuki NAKAJIMA is supported by projects from JAXA/EarthCARE,MEXT/VL for Climate System Diagnostics,the MOE/Global EnvironmentResearch Fund A-1101, NIES/GOSAT,NIES/CGER,MEXT/RECCA/SALSA,and the S-12 of the MOE.

Open Access.This article is distributed under the terms of the Creative Commons Attribution License which perm its any use,distribution,and reproduction in any medium,provided the original author(s)and the source are credited.

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:Dai,T.,G.Y.Shi,and T.Nakajima,2015:Analysis and evaluation of the globalaerosoloptical properties simulated by an online aerosol-coupled non-hydrostatic icosahedralatmospheric model.Adv.Atmos.Sci.,32(6),743–758,

10.1007/s00376-014-4098-z.

(Received 25 August2014;revised 22 October 2014;accepted 15 November2014)

∗Corresponding author:DAITie

Email:daitie@mail.iap.ac.cn

©The Authors 2015