Impacts of future air pollution mitigation strategies on the aerosol direct radiative forcing over Europe

Impacts of future air pollution mitigation strategies on the aerosol direct radiative forcing over Europe

Atmospheric Environment 62 (2012) 451e460 Contents lists available at SciVerse ScienceDirect Atmospheric Environment journal homepage: www.elsevier...

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Atmospheric Environment 62 (2012) 451e460

Contents lists available at SciVerse ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Impacts of future air pollution mitigation strategies on the aerosol direct radiative forcing over Europe J.C. Péré a, *, A. Colette a, P. Dubuisson b, B. Bessagnet a, M. Mallet c, V. Pont c a

Institut National de l’Environnement Industriel et des Risques, Parc technologique Alata, 60550 Verneuil en Halatte, France Laboratoire d’Optique Atmosphérique, Université des Sciences et Technologies de Lille, 59655 Villeneuve d’Ascq, France c Laboratoire d’Aérologie, CNRS, Université Paul Sabatier, Toulouse, France b

h i g h l i g h t s < We developed a coupling between chemistry-transport and radiative transfer models. < We simulated the Aerosol Direct Radiative Forcing at regional scale. < We studied the ADRF response to 2 emission reduction scenarios for 2030 over Europe. < Measures to improve air quality could have implication on aerosol climate forcing.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 February 2012 Received in revised form 19 August 2012 Accepted 21 August 2012

Projections of aerosol emissions for 2030 have been recently generated and implemented in a comprehensive chemistry-transport model to analyse the future evolution of the aerosol radiative forcing over Europe. In this study, numerical developments based on an off-line coupling between the regional chemistry-transport model CHIMERE (extended by an aerosol optical module) and the radiative transfer code GAME have been implemented in order to simulate the interaction of physico-chemically resolved aerosols with radiation at regional scale. This novel approach is used to examine the shortwave aerosol direct radiative forcing response to two air pollution reduction scenarios for 2030 over Europe. Our study suggests that measures introduced to improve future air quality could have large implication on the aerosol climate forcing over Europe. Results of simulations indicate that abatement of aerosols in the near future could lead to a decrease of the aerosol cooling effect at the surface and at the top of the atmosphere over the main anthropogenic emission regions. Especially over the Moscow region, different strategies of reduction for scattering sulphate and absorbing black carbon aerosols between the two scenarios could result, however, in either a reduction or an enhancement in atmospheric radiative forcing. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Aerosol Air quality Climate Emission reduction Direct radiative forcing Cooling/warming effects

1. Introduction Aerosol particles play an important role on climate. They have a direct radiative impact by scattering and absorbing solar and terrestrial radiation, which can result in cooling or warming of the atmosphere (Trenberth et al., 2009; Wang et al., 2009). Moreover, they can indirectly modify the earth’s radiative budget by changing the reflectance and persistence of clouds (indirect and semi-direct effects) (Lohmann and Feichter, 2005; Koch and Genio, 2010). Aerosols are also known to have adverse health impacts such as respiratory and cardiovascular diseases (Delfino et al., 2005; * Corresponding author. E-mail address: [email protected] (J.C. Péré). 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.08.046

Ramgolam et al., 2009). Consequently, in many regions of the world, emissions of aerosols and their precursor gases are reduced (Amann et al., 2011a). However, these regulations have to take into account the effect on climate. For instance, black carbon aerosols are known to absorb incoming solar radiation and contribute to a significant warming of the atmosphere (Ramanathan and Carmichael, 2008) while sulphate aerosols are known to scatter incoming solar radiation and contribute to the cooling of the atmosphere (Marmer et al., 2007a). Given that short-lived climate forcers can have opposite effects on climate, carefully designed regulatory approaches have then to be undertaken to mitigate both the impact of particles on climate and improve air quality (Ländahl et al., 2010; Perkins, 2010). A useful metric to investigate the potential impacts of aerosol emission reductions on climate is the radiative forcing (Forster

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et al., 2007), which is a measure of the modification of the radiative balance of the climate system caused by an external perturbation such as changes in aerosol concentration. The impacts of aerosol reductions on radiative forcing are usually investigated with global climate models, which include some detailed parameterizations of the interaction of particles with radiation (Unger et al., 2008; Kloster et al., 2010; Shindell and Faluvegi, 2010; Lamarque et al., 2011). Since aerosols are emitted by a variety of regionnaly spread sources, these large scales models are however less appropriate to estimate regional changes in the aerosol direct radiative forcing over hotspots of air pollution. In a different approach, the GAINS model (Greenhouse gaseAir Pollution Interactions and Synergies, Amann (2009)), developed at IIASA (International Institute for Applied System Analysis) has been recently used to analyse how future emissions changes over Europe could impact the aerosol radiative forcing on a regional scale (Amann et al., 2011b). This model considers detailed emission reduction strategies for each individual country. Nevertheless, the calculation of the aerosol radiative forcing, based on linear functions of aerosol emissions and concentrations (Amann et al., 2011b), is simplified in terms of representativeness of the interaction of aerosols with radiation. In this study, we propose a new modelling approach to simulate explicitly the transport, physico-chemical evolution and radiative impacts of aerosols at regional scale. It consists of an off-line coupling between the regional chemistry-transport model CHIMERE (Vautard et al., 2001), extended by an aerosol optical module (Péré et al., 2010), and the radiative transfer code GAME (Dubuisson et al., 1996). This methodology enables us to discuss the issue of competing benefits of reducing aerosols over European hotspots of air pollution by using two specific state-of-the-art models associated with emission reduction scenarios specifically designed for regional studies. One limitation of our study is that clouds, which can modulate the amount of solar radiation interacting with particles (Satheesh, 2002), are not taken into account in our simulations. Here, we have focused our study on the aerosol direct radiative forcing (ADRF) for clear-sky conditions. This approach is consistent with recent studies investigating changes in the clear-sky ADRF associated to changes in aerosol emissions and physico-chemical properties (Babu et al., 2008; Lamarque et al., 2011). Also, the ADRF is estimated in the shortwave region since interaction of particles with longwave radiation is shown to be negligible in case of small anthropogenic aerosols (Ramanathan and Feng, 2009). The paper is structured as follows: In Section 2, the off-line coupling between CHIMERE and GAME are described and the modelling set-up is presented. Modelling results are discussed in Section 3 and concluding remarks are given in Section 4. 2. Methodology 2.1. Off-line coupling between CHIMERE and GAME 2.1.1. Description of the CHIMERE model CHIMERE is a three-dimensional chemistry-transport model developed to simulate gas-phase chemistry, aerosol formation, transport and deposition at regional and urban scales (Vautard et al., 2001; Bessagnet et al., 2004). The modelling system and a complete documentation can be downloaded via the internet web site: http://www.lmd.polytechnique.fr/chimere/. The aerosol module takes into account 9 species: sulphate, nitrate, ammonium, primary organic (OC) and black carbon (BC), secondary organic aerosols (SOA), sea salt, dust and water, that are distributed within 8 bins ranging from 40 nm to 20 mm. The main physical processes influencing aerosol population such as nucleation of sulphuric acid, coagulation, condensation/evaporation,

adsorption/desorption, wet and dry deposition and scavenging are included in the model and described in Bessagnet et al. (2004). Initial and boundary conditions for gaseous and particulate pollutants are taken from monthly climatologies of the LMDzTINCA (Hauglustaine et al., 2004) model. The meteorological variables required by CHIMERE (3D wind, air temperature, relative humidity, .) are provided by the Weather Research and Forecasting (WRF) model (Skamarock et al., 2001) at 50 km resolution. CHIMERE has been extensively evaluated in simulating gaseous and particulate pollutants at the European (Bessagnet et al., 2004; Honoré et al., 2008; Bessagnet et al., 2009) and regional (Hodzic et al., 2005; Monteiro et al., 2007; Fouquet et al., 2011) scales and for long time periods (Colette et al., 2011). 2.1.2. Aerosol optical properties Optical properties of particles are required to evaluate their radiative impact. A description of the scheme designed to calculate aerosol optical properties from concentrations, size distribution and chemical composition can be found in Péré et al. (2010). Computing the complex refractive index of a particle requires the assumption on the mixing state of the aerosol chemical species. Here, we consider an aerosol coreeshell mixing state where each particle is composed by a core of primary species (BC, OC and dust) surrounded by a shell of secondary species (sulphates, nitrates, ammonium, secondary organics), sea salt and water. Such coatings of secondary particles on black carbon aerosols have been recently observed over Europe by Vester et al. (2007) and McMeeking et al. (2011). For each size bin, the real and imaginary parts of the refractive index of the core and the shell have been determined using a volume average procedure (Lesins et al., 2002). The volume of the core and the shell can vary during the simulation as volume of chemical species will depend on physical processes influencing aerosol population such as nucleation, coagulation, condensation/ evaporation, adsorption/desorption and deposition. The Mie algorithm for n-layered spheres of Wu and Wang (1991) is used to compute the scattering and absorption coefficients of a coreeshell particle. The optical properties of the total aerosol distribution are then calculated by using the methodology of Wu et al. (1996). In this study, parameters required are the Aerosol Optical Thickness (AOT), Single Scattering Albedo (SSA) and the asymmetry parameter (g). Detailed evaluations of the optical module using EMEP/AirBase observations (PM10/PM2.5, nitrate, sulphate, ammonium), AERONET sunphotometers measurements (AOT, SSA, g, aerosol size distribution) and satellite data (MODIS AOT) can be found in Péré et al. (2009, 2010). 2.1.3. Clear-sky aerosol direct radiative forcing The clear-sky aerosol direct radiative forcing has been estimated in the shortwave region with the radiative transfer code GAME (Global Atmospheric ModEl) described in Dubuisson et al. (2006). Rayleigh scattering and absorption by major gaseous species such as water vapour, carbon dioxide, methane and ozone are taken into account in GAME. Calculation of absorption process is based on the results of a line by line code (Scott, 1974) and scattering process is treated with the discrete ordinate method (Stamnes et al., 1988), which allows an accurate treatment of multiple scattering effects by employing a Legendre polynomial decomposition for the phase function and radiance. Coefficients of the correlated k-distribution have been estimated from reference calculations using a line-by-line code (Dubuisson et al., 1996, 2004). Upward and downward net radiative fluxes are computed over the shortwave solar spectrum (from 0.2 mm to 4 mm) for 18 vertical levels (from 40 m to 20 km) at one hour interval. Vertical profiles of air temperature, pressure and relative humidity required by GAME have been provided by WRF. GAME also uses monthly mean values of the terrestrial albedo from

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NCEP climatologies. This assumption should be evaluated in future studies, as possible modification of surface reflection in the context of climate change could have an influence on the estimation of the aerosol direct radiative forcing (Satheesh, 2002). To take into account the impact of aerosol solar extinction on the radiative fluxes, the aerosol optical properties (AOT, SSA and g) have been simulated using CHIMERE and then used as input to the GAME radiative transfer code. AOT, SSA and g have been estimated for the 8 vertical layers of the CHIMERE model ranging from 40 m to about 6 km above ground level. For higher altitude, a climatology of optical properties for the free troposphere and the stratosphere (Hess et al., 1998) has been used (Table 1). In this climatology, AOT is characterized by a decrease from 0.025 to 0.002 and from 0.010 to 0.001 between 0.2 and 4.0 mm for aerosols located within the free troposphere and the stratosphere, respectively. Also, particles are considered to be mostly scattering with constant SSA values of 0.94 (for the free troposphere) and 1 (for the stratosphere) in the UVe visible-near-infrared region. A constant value of 0.7 for the asymmetry parameter is used between 0.2 and 4.0 mm above an altitude of 6 km. To investigate the relative contribution of free tropospheric and stratospheric particles to the ADRF, Fig. 1 presents the vertical profiles of aerosol extinction modelled by CHIMERE (at 440 nm) with the reference scenario and averaged over the year 2005 for the sites of Leipzig, Moscow and Palaiseau (see Fig. 2 for their locations). We can see that over the three sites, the aerosol extinction modelled by CHIMERE is maximum near the ground where particles are emitted with values ranging from 50$106 to 59$106 m1, decreasing up to 300e500 m and more gradually up to an altitude of about 5 km. Above, the aerosol extinction issued from the climatology of Hess et al. (1998) is found to be negligible, which suggests a low influence of free tropospheric and stratospheric aerosols in determining the ADRF. For each emission scenario considered, two radiative forcing post-processings were performed: including or excluding the impact of aerosols on solar extinction. From these two estimates, we compute the aerosol direct radiative forcing at the bottom of the atmosphere (DFBOA) and at the top of the atmosphere (DFTOA) as follows: w o DFBOA ¼ FBOA  FBOA



w o DFTOA ¼  FTOA  FTOA

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Fig. 1. Vertical profiles of aerosol extinction (in Mm1) modelled by CHIMERE (at 440 nm) with the reference scenario and averaged over the year 2005 for the sites of Leipzig, Moscow and Palaiseau.

(1) 

(2)

o where Fw BOA and FBOA are, respectively, the net radiative fluxes at the o surface simulated with (w) and without (o) aerosols. Fw TOA and FTOA are, respectively, the net radiative fluxes at the top of the atmosphere simulated with and without aerosols. Finally, the atmospheric radiative forcing (DFATM) is calculated with the following relation:

DFATM ¼ DFTOA  DFBOA

(3)

where DFATM represents the absorption of solar radiation by aerosols within the atmospheric layer. This increase in energy within Table 1 Climatology of aerosol optical properties (aerosol optical thickness, single scattering albedo and asymmetry parameter) for the free troposphere and the stratosphere issued from Hess et al. (1998). Free troposphere (6e12 km)

AOT SSA g

Stratosphere (12e20 km)

0.3 mm

0.4 mm

0.6 mm

0.99 mm

0.3 mm

0.4 mm

0.6 mm

0.99 mm

0.025 0.94 0.7

0.019 0.94 0.7

0.011 0.94 0.7

0.006 0.94 0.7

0.010 1 0.7

0.007 1 0.7

0.005 1 0.7

0.001 1 0.7

Fig. 2. Annual mean AOT for 2005 (a) modelled by CHIMERE (at 440 nm) with the reference scenario and (b) retrieved by the MODIS sensor (at 550 nm) and sunphotometers (at 440 nm) at six AERONET stations*. *BAR: Barcelona (2.07  E, 41.23  N), CAB: Cabauw (4.55  E, 51.58  N), LEI: Leipzig (12.26  E, 51.21  N), MOS: Moscow (37.30  E, 55.42  N), PAL: Palaiseau (2.12  E, 48.42  N), ROM: Rome (12.38  E, 41.50  N).

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the atmospheric layer can, in turn, lead to a radiative heating of this layer, which can be estimated by the vertical profile of the heating rate given at an altitude z by:

discuss future changes in the aerosol budget, optical properties and clear-sky direct radiative forcing simulated with the LOWCLE and HIGHCLE scenarios as compared to the 2005 reference.

vT 1 vFðzÞ  ðzÞ ¼ rCp vt vz

3. Results and discussion

(4)

where T is the radiative temperature of the air, r the air density, Cp is the specific heating capacity of the air and F(z) the net radiative flux at the altitude z. In the following, the estimation of the clear-sky ADRF will be based on 24 h-averaged values, which is a methodology commonly reported in the literature (Saha et al., 2008; Myhre et al., 2009; Alam et al., 2011; Malavelle et al., 2011). 2.2. Modelling set-up The emission scenarios used in the present study are those of the Global Energy Assessment (GEA) delivered by IIASA. These scenarios were designed to help decision makers address the challenges of providing energy services for sustainable development. Three scenarios are investigated in this study: (1) The 2005 reference scenario includes current anthropogenic emissions for the year 2005. (2) The HIGHCLE emission scenario is based on current and planned air pollution policies until 2030 in the absence of any specific climate or energy access policy. (3) The LOWCLE emission scenario also assumes a complete implementation of current and planned air pollution policies until 2030. Additionally, it includes specific policy on energy demand (clean energy) and uses the implementation of a stringent climate policy corresponding to a 2 increase of the global temperature by 2100. Thus, this scenario explicitly provides an indication of the co-benefits of combining policies on climate change, energy access and air pollution. A complete description of these scenarios can be found in Riahi et al. (2012). For each emission scenario considered, simulations are performed over the 1998e2002 meteorological years for the     European domain (36 N to 65 N, 15 E to 40 E) at 50 km resolution with the same set-up as in the work of Colette et al. (2011) and summarized in Table 2. It should be noted that emissions remain constant throughout the simulated period. Also, the use of current meteorological conditions enables us to isolate the role of aerosol emissions on the estimation of the ADRF. In the following, we will Table 2 Technical characteristics of the CHIMERE set-up used in the present study. Chemical mechanism

Gas-phase Aerosols

Geometry

Meteorology

Modeling domain (resolution) Number of vertical levels (min, max) Meso-scale model

Large scale forcing Boundary conditions Biogenic emissions Natural emissions Biomass burning emissions

Melchior 2: 44 species, 120 reactions (Lattuati, 1997) ISORROPIA, 9 aerosol species, 8 bins (Nenes et al., 1998) Europe (0.5 ) 8 (40 m, 6 km) WRF V3.2.1 (Skamarock et al., 2001) at 50 km resolution ERA-Interim LMDTz-INCA monthly climatologies (1997e2001) MEGAN V2.0.4 (Guenther et al., 2006) none GFED monthly (A. Heil, personnal communication, 2010)

3.1. Evaluation of the aerosol optical properties To evaluate uncertainties on modelled aerosol optical properties, two sensitivity tests have been performed with the 2005 reference scenario. Priority is given to the choice of the complex refractive index of black carbon, which is the main absorbing aerosol species. Simulations have been performed with BC refractive index values of 1.75-i0.44 (Hess et al., 1998) and 1.95-i0.66 (Bergström, 1972). Estimates of the simulated optical properties are compared with those obtained with the reference value of 1.87i0.569 (Marley et al., 2001) used thereafter in our simulations. We also evaluated the impact of an increase/decrease of 20% of simulated BC mass concentration on modelled optical properties. These two types of sensitivity tests indicate a small sensitivity of 0.005, 0.001 and 0.03, respectively, for modelled aerosol optical thickness, asymmetry parameter and single scattering albedo. The performance of the model in simulating aerosol optical properties were also evaluated by comparing the modelled AOT and SSA to MODIS and AERONET measurements. MODIS is a spectroradiometer on board TERRA and AQUA platforms which derives AOT at 550 nm with a one degree resolution over Europe (King et al., 2003). Uncertainty of MODIS AOT above continents is given by 0.05 þ 0.2  AOT (Kaufman et al., 1997). The AERONET network provides sunphotometers measurements of a large number of parameters characterizing aerosol population such as AOT and SSA (Holben et al., 1998, 2001). In this study, we use level 2.0 (cloud free and manually checked) data at 440, 675, 870 and 1020 nm for six AERONET stations distributed over Western Europe (figure 2). It should be noted that level 1.5 data (only cloud free) were also used when level 2.0 was not available. The uncertainties in AERONET measurements are 0.01 for AOT (l ¼ 440 nm) and 0.03 when the AOT (440 nm) is larger than 0.2 and 0.07 otherwise for SSA (Dubovik et al., 2000; Holben et al., 2001). Based on these considerations, we estimate in our study an average uncertainty on AERONET SSA of 0.05. The annual mean values of the AOT for 2005, modelled by CHIMERE (at 440 nm) with the reference scenario and retrieved by MODIS (at 550 nm) and sunphotometers (at 440 nm) at six AERONET stations are displayed in Fig. 2. CHIMERE AOT (440 nm) is characterized by low modelled values over southern France, the Alps and north-western Spain (0.030e0.060  0.005) and larger values over Italy, Benelux, Germany and Eastern Europe (0.08e 0.14  0.005), which are known to be among the hot-spots of pollution in Europe (Memmesheimer et al., 2004). It is noteworthy that the AOT intensity is underestimated by a factor of 2e4 over a large part of the domain when compared to AERONET (0.19e 0.33  0.01 at 440 nm) and MODIS (0.15e0.35  0.08e0.12 at 550 nm) observations. It should be noted that the direct comparison between observations and simulations could be affected by the presence of clouds. Indeed, AERONET and MODIS AOT are only retrieved for cloud-free conditions whereas modelled AOT is averaged for all days of 2005. In addition, some aerosol sources missing in the GEA emissions could also explain the model biases. For example, primary particulate matter (PPM) is only composed of fine black and primary organic carbon. They do not include other constituents such as heavy metals or the coarse fraction of PPM (above 2.5 mm in diameter), which can significantly contribute to the total mass of particles over European areas (Amato et al., 2009; Putaud et al., 2010; Karanasiou et al., 2011).

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Fig. 3 presents the wavelength dependence of the annual mean AOT for 2005 simulated by CHIMERE with the reference scenario along with corresponding sunphotometers measurements at the six selected AERONET stations. Values of modelled and observed Angström exponent (calculated between 440 and 1020 nm) are also indicated. We can note that although the spectral dependence of the modelled AOT is lower than that of AERONET observations (especially between 440 and 675 nm), the model simulates a decrease of aerosol solar extinction across the visible-near infrared spectrum. Such a model behaviour is corroborated by the good agreement between modelled and observed Angström exponent (440e 1020 nm) over the selected sites (4% ¼ biases ¼ 21%). This indicates that the model is able to reproduce the fine size mode of aerosols, which has been shown to be a major contributor to the shortwave direct radiative forcing over Europe (Marmer et al., 2007b; Bergamo et al., 2008; Zanis et al., 2012). The simulated SSA show values (annual mean for 2005) ranging from 0.90  0.03 to 0.93  0.03 (at 440 nm), which corresponds to moderately absorbing particles (Fig. 4). These modelled values are within the uncertainty range of observations (0.86e0.93  0.05) except over the site of Cabauw where the SSA is overestimated by the model (SSAchimere (440 nm) ¼ 0.91  0.03 and SSA (440 nm) ¼ 0.81  0.05). To sum up, the evaluation of the aerosol optical module with the 2005 reference scenario exhibits some model deficiencies in simulating the magnitude of AOT (440 nm). However, the fairly good estimation of both the magnitude of the SSA (440 nm) and the spectral dependence of the AOT highlights the ability of the model to give an appropriate representation of the aerosol size distribution and absorbing properties; which is pre-requisite to evaluate the impacts of future aerosol reductions on the shortwave ADRF. 3.2. Changes in the aerosol budget, optical properties and direct clear-sky radiative forcing 3.2.1. Spatially averaged changes Table 3 presents domain-averaged annual mean columnburdens of sulphates, BC, OC and PM10 (in g m2) and AOT (unitless), DFBOA, DFTOA and DFATM (in W m2) simulated with the 2005 reference scenario along with corresponding changes obtained with

Fig. 3. Wavelength dependence of the annual mean AOT for 2005 simulated by CHIMERE with the reference scenario along with corresponding sunphotometers observations at six AERONET stations. The error bars represent the uncertainty range of observations (0.01, see Holben et al. (2001)) and simulations (0.005, see section 3.1). Values of the simulated (red) and observed (black) Angström exponent (440e 1020 nm) are also indicated. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

455

Fig. 4. Annual mean SSA (at 440 nm) for 2005 simulated by CHIMERE with the reference scenario along with corresponding observations at six AERONET stations. The error bars represent the uncertainty range of observations (0.05, estimated from SSA uncertainties given in Dubovik et al. (2000)) and simulations (0.03, see Section 3.1).

the HIGHCLE and LOWCLE scenarios at the horizon 2030. Results are presented as follows: mean  interannual variability. Vertical column (V.C.) of aerosols have been calculated as follows: kX ¼ nl   V:C:i g m2 ¼ Ci ðkÞ  HðkÞ

(5)

k¼1

where Ci(k) (g m3) is the modelled concentration of the aerosol species i in the middle of the vertical layer k, H(k)(m) the width of the vertical layer k and nl the total number of layer. With present day emissions, domain-averaged annual mean PM10 column-burden is simulated as 42  2 g m2 with a contribution of BC, OC and sulphates of, respectively, 1.00  0.05, 3.0  0.1 and 6.9  0.5 g m2. This aerosol load corresponds to a simulated AOT (at 440 nm) of 0.044  0.001, which induces an ADRF at the surface, top of the atmosphere and within the atmospheric layer of, respectively, 2.30  0.04, 1.37  0.03 and 0.93  0.02 W m2. It is interesting to note that our estimates of clear-sky DFTOA for the European domain are larger than those given at a global annual mean in the last IPCC 2007 report (90% confidence level range of (0.1e0.9) W m2, Forster et al. (2007)). Table 3 indicates that in the future, PM10 concentration is shown to decrease substantially in the HIGHCLE simulation (8.0  0.5%) and even more so in the LOWCLE simulation (19  1%) in which aerosol emissions are reduced to a greater extent due to both full implementation of current and planned air pollution legislation and specific policies on climate change. Changes in the PM10 column-burden result in a similar decrease in the AOT (at 440 nm) reaching, respectively, 9 and 14% with the HIGHCLE and LOWCLE scenarios. This decrease in PM10 concentration reflects the reduction, for both scenarios, in emissions of carbonaceous aerosols and anthropogenic SO2 mainly from the domestic, industrial and transport sectors. The decrease in BC, OC concentrations reaches respectively (in annual mean), and SO2 4 41  3, 81  6 and 30  2% for the HIGHCLE scenario and 69  5, 87  6 and 49  4% for the LOWCLE scenario. As expected, future decrease in the AOT leads to a reduction of DFBOA, DFTOA and DFATM reaching, respectively, 6.0  0.4, 7.0  0.5 and 5.3  0.3% for the HIGHCLE scenario and 9.5  0.5, 10.0  0.5 and 8.5  0.3% for the LOWCLE scenario. Such results could have important implication for climate change mitigation as the ADRF has been shown to be one of the factors that could affect water cycle and atmospheric

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Table 3 Domain-averaged annual mean column-burdens of sulphates, BC, OC and PM10 (in g m2) and AOT (unitless), DFBOA, DFTOA and DFATM (in W m2) simulated with the reference scenario (REF) along with corresponding changes obtained with the HIGHCLE and LOWCLE scenarios. Results are presented as follows: mean  interannual variability (numbers in brackets give percentage changes). Note that a positive change in DFBOA and DFTOA indicates a reduction of the ADRF at BOA and TOA. REF 2 SO2 4 (g m ) BC (g m2) OC (g m2) PM10 (g m2) AOT(440 nm) (unitless) DFBOA (W m2) DFTOA (W m2) DFATM (W m2)

6.9 1.00 3.0 42 0.044 2.30 1.37 0.93

       

0.5 0.05 0.1 2 0.001 0.04 0.03 0.02

dynamics at regional scale (Zhang et al., 2010; Kawase et al., 2011; Péré et al., 2011). However, the projected reduction in the aerosol cooling effect at the surface (through reduction of DFBOA), due to the air quality measures, can induce a regional warming of the atmosphere. Indeed, a recent modelling study showed that the removal of aerosols in the future could lead to an increase of the annual mean near surface air temperature by 0.4e0.6 K over the eastern United-States (Mickley et al., 2012). It should be noted that modelled changes in the aerosol vertical column and ADRF have a low interannual variability in both scenarios (Table 3), partly because emission reductions remain constant throughout the simulated period for each scenario. 3.2.2. Spatial patterns of changes To explore the regional impact by future modifications of the aerosol cooling/warming effect, we will now investigate geographical distributions of optical properties changes (AOT, SSA)

HIGHCLE minus REF

LOWCLE minus REF

2.3  0.1 0.42  0.04 2.4  0.1 4.7  0.2 0.0041  0.0002 0.14  0.01 0.100  0.005 0.050  0.002

3.8  0.2 0.70  0.05 2.7  0.1 8.0  0.5 0.0062  0.0002 0.21  0.01 0.140  0.008 0.080  0.001

(30  2) (41  3) (81  6) (8.0  0.5) (9  1) (6.0  0.4) (7.0  0.5) (5.3  0.3)

(49  4) (69  5) (87  6) (19  1) (14  1) (9.5  0.5) (10.0  0.5) (8.5  0.3)

and the related ADRF response for two seasons: winter and summer. Due to the low interannual variability of predicted changes, the results presented hereafter will be averaged over the entire period. Fig. 5 presents the future evolution of the AOT (440 nm) (left) and DFBOA (in W m2) (right) as the difference between the LOWCLE and the reference simulations for winter (upper panel) and summer (lower panel). The decrease of the aerosol optical thickness is found to be largest in summer with most of the impact occurring over the Pô Valley, central Europe and the Balkans (0.016e0.026 x 20e35%). It is noteworthy in Fig. 5 that a large part of the Iberian Peninsula undergoes limited changes in AOT at the horizon 2030, especially in winter. Over this region, both socioeconomical and geophysical factors could explain this finding. In the GEA emission scenarios, the reduction of primary particulate matter is slightly lower in Spain and Portugal than elsewhere. For the LOWCLE scenario, PM2.5 emissions in 2030 are 42% of their

Fig. 5. Changes in AOT at 440 nm (left) and DFBOA (in W m2) (right) as the difference between the LOWCLE and the reference simulations for winter (upper panel) and summer (lower panel).

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2005 levels while it is 38% in France for example. For SOx (sulphur oxides), these numbers are 28% and 25% for Spain and France respectively (Colette et al., 2012). However, this difference in emissions is not large enough to explain the lack of reduction of AOT in Spain and Portugal. Boundaries conditions, that are kept constant for all simulations, play also an important role, and similar lack of reduction are seen elsewhere on the West, South, and North boundaries of the domain. The Eastern boundary is located leeward of the major emission areas and is thus less sensitive to this impact. Over regions where the most reductions in AOT occur (Pô Valley, central Europe), PM2.5 and SOx emissions in the LOWCLE scenario are reduced by more than 50e70%. The reduction of the AOT induces a reduction of the surface ADRF (and hence a reduction of the aerosol cooling effect at the surface) with a maximum change in summer during the longest period of solar radiation. Over the main anthropogenic emission regions, DFBOA is shown to be reduced up to 0.2e0.7 W m2 (15e 25%) in winter and up to 0.7e1.0 W m2 (20e30%) in summer. As expected, we can see in Fig. 5 that the spatial pattern of AOT changes is consistent with the spatial pattern of DFBOA changes. However, the amplitude of changes are not always correlated. For example, DFBOA changes more than AOT over Barcelona, western Germany and the Moscow region during the summer season. Over these regions, the enhanced reduction of the ADRF compared to the AOT could be explained by the different contribution of individual aerosol species (such as black carbon) to the AOT and ADRF. It should be noted that reductions in AOT and DFBOA obtained with the HIGHCLE scenario (not shown) exhibit similar spatial patterns than those simulated with the LOWCLE scenario, but with a lower magnitude (see Section 3.2.1).

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The climate influence of aerosols is not determined exclusively by change in mass concentration, but also by the resulting change in absorbing properties. To investigate how future changes in the aerosol chemical composition and associated absorbing properties can modify the ADRF, we compare changes in co-SSA (1-SSA) at 440 nm and DFATM as the difference between the LOWCLE/HIGHCLE and the reference simulations, for winter (Fig. 6) and summer (Fig. 7). It should be noted that here, we discuss changes in co-SSA (ratio of the aerosol solar absorption to the aerosol solar extinction) as it is directly related to the absorbing properties of the particles. During the winter season, both scenarios show similar trends with a reduction of co-SSA (440 nm) between 0.010 and 0.035 (15e30%) over the main anthropogenic emission regions. Over these areas, the decrease of absorbing black carbon concentrations (mainly from the reduction of residential heating emissions) tends to dominate the decrease of scattering SO2 4 concentrations. Hence, the reduction of the absorbing efficiency of the aerosol layer results in a decrease of DFATM of 0.1e0.3 W m2 (25e45%), leading to a reduction of the aerosol warming effect within the atmosphere at the horizon 2030. In summer, we can see in Fig. 7 that both scenarios induce similar trends over the western part of the domain, also with a lower aerosol atmospheric forcing ((0.1e 0.7) W m2 x (15e45) %) as a result of the decrease of the co-SSA (440 nm) (0.010e0.050 x (20e40) %). However, it is interesting to note that over the eastern part of the domain and especially over the Moscow and Minsk region, opposite trends are simulated between the two scenarios in summer. During this period, an increase of co-SSA (0.015e0.030 x 30e45%) is found with the HIGHCLE scenario, which reflects the smaller decrease of absorbing BC emissions compared to SO2 emissions (precursor of

Fig. 6. Changes in co-SSA (1-SSA) at 440 nm (left) and DFATM (in W m2) (right) as the difference between the LOWCLE/HIGHCLE and the reference simulations (upper/lower panel) for winter.

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Fig. 7. Same as in Fig. 6 but for summer.

scattering SO2 4 ) in the emission inventory used over these areas. As a result, a positive change of DFATM is simulated and reaches 0.2 W m2 (x10e15%) over the Moscow region, which results in an increase of the aerosol warming effect within the atmosphere of 0.14 K day1 (on average over summertime). Such atmospheric radiative heating over Moscow is not simulated with the LOWCLE scenario (Fig. 7) as in that case, the decrease of BC emission tends to dominate the decrease of SO2emission even in summer, leading to a reduction of both co-SSA (440 nm) (0.010e0.030 x (20e40) %) and DFATM (0.4e0.5 W m2 x (20e30) %). This result is important as it illustrates that aerosol abatement policies that aim at reducing the ratio of absorbing to scattering aerosols can avoid an additional warming of the atmosphere (Ramana et al., 2010; Bahadur et al., 2011). 4. Conclusions In this paper, we discuss changes in the ADRF resulting from future projections of aerosol emissions over Europe. We focus on the ADRF in the shortwave solar range and for clear-sky conditions only. The methodology is based on an off-line coupling between the regional chemistry-transport model CHIMERE (extended by an aerosol optical module) and the radiative transfer code GAME. To take into account the impact of aerosol solar extinction on the radiative fluxes, the aerosol optical properties have been simulated using CHIMERE up to 6 km (a climatology has been used for higher altitude) and then used as input in the GAME radiative transfer code. Such methodology constitutes a novel approach as aerosol radiative forcing is usually estimated either with global scale models, that are less appropriate to focus on regional scale, or with regional coupled weather and chemistry models that are limited to

short time periods because of computational costs. We use two different GEA emission reduction scenarios for the year 2030 recently developed by IIASA (HIGHCLE and LOWCLE) and analyse their impact in terms of clear-sky shortwave ADRF compared to present-day conditions. Implementation of future aerosol and aerosol precursors mitigation policies lead to an annual mean decrease of the aerosol cooling effect over the European domain of 0.14 W m2 (6%) and 0.21 W m2 (10%) at the surface and 0.10 W m2 (7%) and 0.14 W m2 (10%) at the top of the atmosphere for, respectively, the HIGHCLE and LOWCLE scenarios. The decrease would be more pronounced with the LOWCLE scenario in which aerosol emissions are reduced to a greater extent due to the full implementation of current and planned air pollution legislation as well as specific policies on climate change. On a seasonal basis, DFBOA is reduced by 0.2e0.7 W m2 in winter and by 0.7e1.0 W m2 in summer over the main anthropogenic emission regions. The ADRF can also be modified by changes in the aerosol chemical composition. In winter, the enhancement of the scattering efficiency of the aerosol layer results in a decrease of DFATM (0.2e 0.3 W m2 x 30e40%) for both scenarios. However, different and absorbing black strategies of reduction for scattering SO2 4 carbon particles in summer could induce either a reduced (with the LOWCLE scenario) or an enhanced (with the HIGHCLE scenario) atmospheric radiative forcing, especially over the Moscow region. Our study suggests that efficient air pollution abatement policies should reduce both the aerosol concentration and its solar absorbing efficiency to avoid additional aerosol-induced warming of the atmosphere. Further modelling studies should also include possible modifications of the aerosol semi-direct and indirect effects in order to better assess future changes in the aerosol cooling/warming effect over Europe.

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Acknowledgements This work was funded by the European Union’s Seventh Framework Programme (FP7/2007e2013) under grant agreement no. 5 212095 (CITYZEN). The authors would like to thank the AERONET and MODIS teams for use of their data and the NOAA/ NCEP/NCAR community for providing the WRF model used to drive the chemistry-transport model. The authors also wish to thank the IIASA members for their work in the development of the emission scenarios used in this study. References Alam, K., Trautmann, T., Blaschke, T., 2011. Aerosol optical properties and radiative forcing over mega-city Karachi. Atmospheric Research 101, 773e782. Amann, M., 2009. Integrated assessment tools. the greenhouse and air pollution interactions and synergies (GAINS) model. Pollution Atmosphérique Special issue, 73e79. Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Hoeglund-Isaksson, L., Klimont, Z., Nguyen, T.B., Posch, M., Rafaj, P., Sandler, R., Schoepp, W., Wagner, F., Winiwarter, W., 2011a. Cost-effective control of air quality and greenhouse gases in Europe: modeling and policy applications. Environmental Modelling and Software 26, 1489e1501. Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Hoglund, L., Klimont, Z., Rafaj, P., Schöpp, W., Wagner, F., 2011b. Cost-effective Emission Reductions to Improve Air Quality in Europe in 2020. Scenarios for the Negotiations on the Revision of the Gothenburg Protocol under the Convention on Long-range Transboundary Air Pollution. Technical Report. IIASA. Amato, F., Pandolfi, M., Escrig, A., Querol, X., Alastuey, A., Pey, J., Perez, N., Hopke, P.K., 2009. Quantifying road dust resuspension in urban environment by Multilinear engine: a comparison with PMF2. Atmospheric Environment 43, 2770e2780. Babu, S.S., Nair, V.S., Moorthy, K.K., 2008. Seasonal changes in aerosol characteristics over Arabian Sea and their consequence on aerosol short-wave radiative forcing: results from ARMEX field campaign. Journal of Atmospheric and Solar Terrestrial Physics 70, 820e834. Bahadur, R., Feng, Y., Russell, L.M., Ramanathan, V., 2011. Impact of california’s air pollution laws on black carbon and their implications for direct radiative forcing. Atmospheric Environment 45, 1162e1167. Bergamo, A., Tafuro, M., Kinne, S., Tomasi, F.D., Perrone, M.R., 2008. Monthly-averaged anthropogenic aerosol direct radiative forcing over the Mediterranean based on AERONET aerosol properties. Atmospheric Chemistry and Physics 8, 6995e7014. Bergström, R.W., 1972. Predictions of the spectral absorption and extinction coefficients of an urban air pollution model. Atmospheric Environment 6, 247e258. Bessagnet, B., Hodzic, A., Vautard, R., Beekmann, M., Cheinet, S., Honoré, C., Liousse, C., Rouil, L., 2004. Aerosol modeling with CHIMEREdPreliminary evaluation at the continental scale. Atmospheric Environment 38, 2803e2817. Bessagnet, B., Menut, L., Curci, G., Hodzic, A., Guillaume, B., Liousse, C., Moukhtar, S., Pun, B., Seigneur, C., Schulz, M., 2009. Regional modeling of carbonaceous aerosols over EuropeeFocus on secondary organic aerosols. Journal of Atmospheric Chemistry 61, 175e202. Colette, A., Granier, C., Hodnebrog, O., Jakobs, H., Maurizi, A., Nyiri, A., Bessagnet, B., D’Angiola, A., D’Isidoro, M., Gauss, M., Meleux, F., Memmesheimer, M., Mieville, A., Rouïl, L., Russo, F., Solberg, S., Stordal, F., Tampieri, F., 2011. Air quality trends in Europe over the past decade: a first multi-model assessment. Atmospheric Chemistry and Physics 11, 11657e11678. Colette, A., Koelemeijer, R., Mellios, G., Schucht, S., Péré, J.C., Kouridis, C., Bessagnet, B., Eerens, H., Velze, K.V., Rouïl, L., 2012. Cobenefits of Climate and Air Pollution Regulations, the Context of the European Commission Roadmap for Moving to a Low Carbon Economy in 2050. Technical Paper of the European Topic Centre on Air Pollution and Climate Change Mitigation (ETC/ACM). Delfino, R.J., Sioutas, C., Malik, S., 2005. Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health. Environmental Health Perspectives 113, 934e946. Dubovik, O., Smirnov, A., Holben, B., King, M., Kaufman, Y., Eck, T., Slutsker, I., 2000. Accuracy assessments of aerosol optical properties retrieved from aerosol robotic network (aeronet) sun and sky radiance measurements. Journal of Geophysical Research 105, 9791e9806. Dubuisson, P., Buriez, J.C., Fouquart, Y., 1996. High spectral resolution solar radiative transfer in absorbing and scattering media: application to the satellite simulation. Journal of Quantitative Spectroscopy and Radiative Transfer 55, 103e126. Dubuisson, P., Dessailly, D., Vesperini, M., Frouin, R., 2004. Water vapor retrieval over ocean using near-IR imagery. Journal of Geophysical Research 109, D19106. http://dx.doi.org/10.1029/2004JD004516. Dubuisson, P., Roger, J.C., Mallet, M., Dubovik, O., 2006. A code to compute the direct solar radiative forcing: application to anthropogenic aerosols during the Escompte experiment. In: Fischer, H., Sohn, B.J. (Eds.), Proceedings of IRS 2004: Current Problems in Atmospheric Radiation. A.D.P., pp. 127e130. August 23e28, Busan, Korea. Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.W., Haywood, J., Lean, J., Lowe, D.C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M.,

459

Dorland, R.V., 2007. Changes in atmospheric constituents and in radiative forcing. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: the Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC Report. Fouquet, C., Malherbe, L., Ung, A., 2011. Geostatistical analysis of the temporal variability of ozone concentrations. Comparisons between CHIMERE model and surface observations. Atmospheric Environment 45, 3434e3446. Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C., 2006. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmospheric Chemistry and Physics 6, 3181e3210. Hauglustaine, D.A., Hourdin, F., Jourdain, L., Filiberti, M.A., Walters, S., Lamarque, J.F., Holland, E.A., 2004. Interactive chemistry in the Laboratoire de Météorologie Dynamique general circulation model: description and background tropospheric chemistry evaluation. Journal of Geophysical Research 109, D04314. http://dx.doi.org/10.1029/2003JD003957. Hess, M., Koepke, P., Schult, I., 1998. Optical properties of aerosols and clouds: the software package OPAC. Bulletin of the American Meteorological Society 79, 831e844. Hodzic, A., Vautard, R., Bessagnet, B., Lattuati, M., Moreto, F., 2005. Long-term urban aerosol simulation versus routine particulate matter observations. Atmospheric Environment 39, 5851e5864. Holben, B., Eck, T., Slutsker, I., Tanré, D., Buis, J., Setzer, A., Vermote, E., Reagan, J., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., Smirnov, A., 1998. Aeronet: a federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment 66, 1e16. Holben, B.N., Tanré, D., Smirnov, A., Eck, T.K., Slutsker, I., Abuhassan, N., Newcomb, W.W., Schafer, J.S., Chatenet, B., Lavenu, F., Kaufman, Y.J., Castle, J.V., Setzer, A., Markham, B., Clarck, D., Frouin, R., Halthore, R., Kameli, A., O’Neil, N.T., Pietras, C., Pinker, R.T., Vass, K., Zibordi, G., 2001. An emerging ground-based aerosol climatology: aerosol optical depth from AERONET. Journal of Geophysical Research 106, 12067e12097. Honoré, C., Rouil, L., Vautard, R., Beekmann, M., Bessagnet, B., Dufour, A., Elichegaray, C., Flaud, J.M., Malherbe, L., Meleux, F., Menut, L., Martin, D., Peuch, A., Peuch, V.H., Poisson, N., 2008. Predictability of European air quality: assessment of 3 years of operational forecasts and analyses by the PREV’AIR system. Journal of Geophysical Research 113, D04301. http://dx.doi.org/10.1029/2007JD008761. Karanasiou, A., Moreno, T., Amato, F., Lumbreras, J., Narros, A., Borge, R., Tobias, A., Boldo, E., Linares, C., Pey, J., Reche, C., Alastuey, A., Querol, X., 2011. Road dust contribution to PM levels e Evaluation of effectiveness of street washing activities by means of Positive Matrix Factorization. Atmospheric Environment 45, 2193e2201. Kaufman, Y.J., Tanré, D., Remer, L., Vermote, E., Chu, A., Holben, B.N., 1997. Operational remote sensing of tropospheric aerosol over the land from EOSeMODIS. Journal of Geophysical Research 102, 17051e17068. Kawase, H., Takemura, T., Nozawa, T., 2011. Impact of carbonaceous aerosols on precipitation in tropical Africa during the austral summer in the twentieth century. Journal of Geophysical Research 116, D18116. http://dx.doi.org/10.1029/ 2011JD015933. King, M.D., Menzel, W.P., Kaufman, Y.J., Tanré, D., Gao, B.C., Platnick, S., Ackerman, S.A., Remer, L.A., Pincus, R., Hubanks, P.A., 2003. Cloud and aerosol properties, precipitable water, and profiles of temperature and humidity from MODIS. IEEE Transactions on Geoscience and Remote Sensing 41, 442e458. Kloster, S., Dentener, F., Feichter, J., Raes, F., Lohmann, U., Roeckner, E., FischerBruns, I., 2010. A GCM study of future climate response to aerosol pollution reductions. Climate Dynamics 34, 1177e1194. Koch, D., Genio, A.D.D., 2010. Black carbon semi-direct effects on cloud cover: review and synthesis. Atmospheric Chemistry and Physics 10, 7685e7696. Lamarque, J.F., Kyle, G.P., Meinshausen, M., Riahi, K., Smith, S.J., van Vuuren, D.P., Conley, A.J., Vitt, F., 2011. Global and regional evolution of short-lived radiatively-active gases and aerosols in the representative concentration pathways. Climatic Change 109, 191e212. Ländahl, J., Swietlicki, E., Lindgren, E., Loft, S., 2010. Aerosol exposure versus aerosol cooling of climate: what is the total health outcome? Atmospheric Chemistry and Physics 10, 9441e9449. Lattuati, M., 1997. Contribution à l’étude du bilan de l’ozone troposphérique à l’interface de l’Europe et de l’Atlantique Nord: modélisation lagrangienne et mesures en altitude. Thèse de sciences Université Paris 6, France. Lesins, G., Chylek, P., Lohmann, U., 2002. A study of internal and external mixing scenarios and its effect on aerosol optical properties and direct radiative forcing. Journal of Geophysical Research 107, 4094. Lohmann, U., Feichter, J., 2005. Global indirect aerosol effects: a review. Atmospheric Chemistry and Physics 5, 715e737. Malavelle, F., Pont, V., Mallet, M., Solmon, F., Johnson, B., Léon, J.F., Liousse, C., 2011. Simulation of aerosol radiative effects over West Africa during DABEX and AMMA SOP-0. Journal of Geophysical Research 116, D08205. http://dx.doi.org/ 10.1029/2010JD014829. Marley, N.A., Gaffney, J.S., Baird, J.C., Blazer, C.A., Drayton, P.J., Frederick, J.E., 2001. An empirical method for the determination of the complex refractive index of size-fractionated atmospheric aerosols for radiative transfert calculations. Aerosol Science and Technology 34, 535e549. Marmer, E., Langmann, B., Fagerli, H., Vestreng, V., 2007a. Direct shortwave radiative forcing of sulfate aerosol over Europe from 1900 to 2000. Journal of Geophysical Research 112, D23S17. http://dx.doi.org/10.1029/2006JD008037.

460

J.C. Péré et al. / Atmospheric Environment 62 (2012) 451e460

Marmer, E., Langmann, B., Hungershofer, K., Trautmann, T., 2007b. Aerosol modeling over Europe: 2. Interannual variability of aerosol shortwave direct radiative forcing. Journal of Geophysical Research 112, D23S16. http:// dx.doi.org/10.1029/2006JD008040. McMeeking, G.R., Morgan, W.T., Flynn, M., Highwood, E.J., Turnbull, K., Haywood, J., Coe, H., 2011. Black carbon aerosol mixing state, organic aerosols and aerosol optical properties over the United Kingdom. Atmospheric Chemistry and Physics 11, 9037e9052. Memmesheimer, M., Friese, E., Ebel, A., Jakobs, H.J., Feldmann, H., Kessler, C., Piekorz, G., 2004. Long-term simulations of particulate matter in Europe on different scales using sequential nesting of a regional model. International Journal of Environment and Pollution 22, 108e132. Mickley, L.J., Leibensperger, E.M., Jacob, D.J., Rind, D., 2012. Regional warming from aerosol removal over the United States: results from a transient 2010e2050 climate simulation. Atmospheric Environment 46, 545e553. Monteiro, A., Miranda, A.I., Borrego, C., Vautard, R., Ferreira, J., Perez, A., 2007. Longterm assessment of particulate matter using CHIMERE model. Atmospheric Environment 41, 7726e7738. Myhre, G., Berglen, T.F., Johnsrud, M., Hoyle, C.R., Berntsen, T.K., Christopher, S.A., Fahey, D.W., Isaksen, I.S.A., Jones, T.A., Kahn, R.A., Loeb, N., Quinn, P., Remer, L., Schwarz, J.P., Yttri, K.E., 2009. Radiative forcing of the direct aerosol effect using a multi-observation approach. Atmospheric Chemistry and Physics 9, 1365e1392. Nenes, A., Pilinis, C., Pandis, S.N., 1998. Isorropia: a new thermodynamic equilibrium model for multiphase multicomponent marine aerosols. Aquatic Geochemistry 4, 123e152. Péré, J.C., Mallet, M., Bessagnet, B., Pont, V., 2009. Evidence of the aerosol core-shell mixing state over Europe during the heat wave of summer 2003 by using CHIMERE simulations and AERONET inversions. Geophysical Research Letters 36. http://dx.doi.org/10.1029/2009GL037334. Péré, J.C., Mallet, M., Pont, V., Bessagnet, B., 2010. Evaluation of an aerosol optical scheme in the chemistry-transport model CHIMERE. Atmospheric Environment 44, 3688e3699. Péré, J.C., Mallet, M., Pont, V., Bessagnet, B., 2011. Impact of the aerosol direct radiative forcing (ADRF) on the radiative budget, surface heat fluxes and atmospheric dynamics during the heatwave of summer 2003 over Western Europe. a modelling study. Journal of Geophysical Research 116, D23119. http:// dx.doi.org/10.1029/2011JD016240. Perkins, S., 2010. Health: aerosol effects. Nature Climate Change. http://dx.doi.org/ 10.1038/nclimate1004. Putaud, J.P., Dingenen, R.V., Alastuey, A., Bauer, H., Birmili, W., Cyrys, J., Flentje, H., Fuzzi, S., Gehrig, R., Hansson, H., Harrison, R., Herrmann, H., Hitzenberger, R., Hüglin, C., Jones, A., Kasper-Giebl, A., Kiss, G., Kousa, A., Kuhlbusch, T., Löschau, G., et al., 2010. An European aerosol phenomenology e 3: physical and chemical characteristics of particulate matter from 60 rural, urban and kerbside sites over Europe. Atmospheric Environment 44, 1308e1320. Ramana, M.V., Ramanathan, V., Feng, Y., Yoon, S.C., Kim, S.W., Carmichael, G.R., Schauer, J.J., 2010. Warming influenced by the ratio of black carbon to sulphate and the black-carbon source. Nature Geoscience 3, 542e545. Ramanathan, V., Carmichael, G., 2008. Global and regional climate changes due to black carbon. Nature Geoscience 1, 221e227. Ramanathan, V., Feng, Y., 2009. Air pollution, greenhouse gases and climate change: global and regional perspectives. Atmospheric Environment 43, 37e50.

Ramgolam, K., Favez, O., Cachier, H., Gaudichet, A., Marano, F., Martinon, L., BaezaSquiban, A., 2009. Size-partitioning of an urban aerosol to identify particle determinants involved in the proinflammatory response induced in airway epithelial cells. Particle and Fibre Toxicology 6, 10. http://dx.doi.org/10.1186/ 1743-8977-6-10. Riahi, K., Dentener, F., Gielen, D., Grubler, A., Jewell, J., Klimont, Z., Krey, V., McCollum, D., Pachauri, S., Rao, S., van Ruijven, B., van Vuuren, D.P., Wilson, C., 2012. Energy Pathways for Sustainable Development. In: Global Energy Assessment: Toward a Sustainable Future. Austria and Cambridge University Press, IIASA, Laxenburg, United Kingdom and New York, NY, USA. Available at: http://webarchive.iiasa.ac.at/Research/ENE/GEA/. Saha, A., Mallet, M., Roger, J.C., Dubuisson, P., Piazzola, J., Despiau, S., 2008. One year measurements of aerosol optical properties over an urban coastal site: effect on local direct radiative forcing. Atmospheric Research 90, 195e202. Satheesh, S.K., 2002. Aerosol radiative forcing over land: effect of surface and cloud reflection. Annales Geophysicae 20, 2105e2109. Scott, N.A., 1974. A direct method of computation of the transmission function of an inhomogeneous medium e I: Description of the method. Journal of Quantitative Spectroscopy and Radiative Transfer 14, 691e704. Shindell, D., Faluvegi, G., 2010. The net climate impact of coal-fired power plant emissions. Atmospheric Chemistry and Physics 10, 3247e3260. Skamarock, W.C., Klemp, J.B., Dudhia, J., 2001. Prototypes for the WRF (Weather Research and Forecasting) Model. Ninth Conf. on Mesoscale Processes Fort Lauderdale, FL, Amer. Meteor. Soc.. Stamnes, K., Tsay, S.C., Wiscombe, W., Jayaweera, K., 1988. Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. Applied Optics 27, 2502e2509. Trenberth, K.E., Fasullo, J.T., Kiehl, J., 2009. Earth’s global energy budget. Bulletin of the American Meteorological Society 90, 311e323. Unger, N., Shindell, D.T., Koch, D.M., Streets, D.G., 2008. Air pollution radiative forcing from specific emissions sectors at 2030. Journal of Geophysical Research 113, D02306. http://dx.doi.org/10.1029/2007JD008683. Vautard, R., Beekmann, M., Roux, J., Gombert, D., 2001. Validation of a hybrid forecasting system for the ozone concentrations over the Paris area. Atmospheric Environment 14, 2449e2461. Vester, B.P., Ebert, M., Barnert, E.B., Schneider, J., Kandler, K., Schütz, L., Weinbruch, L., 2007. Composition and mixing state of the urban background aerosol in the Rhein-Main area (Germany). Atmospheric Environment 41, 6102e6115. Wang, Y., Che, H., Ma, J., Wang, Q., Shi, G., Chen, H., Goloub, P., Hao, X., 2009. Aerosol radiative forcing under clear, hazy, foggy, and dusty weather conditions over Beijing, China. Geophysical Research Letters 36, L06804. http://dx.doi.org/ 10.1029/2009GL037181. Wu, X., Seigneur, C., Bergström, R., 1996. Evaluation of a sectional representation of size distributions for calculating aerosol optical properties. Journal of Geophysical Research 101, 19277e19283. Wu, Z.P., Wang, Y.P., 1991. Electromagnetic scattering for multilayered spheres: recursive algorithms. Radio Science 26, 1393e1401. Zanis, P., Ntogras, C., Zakey, A., Pytharoulis, I., Karacostas, T., 2012. Regional climate feedback of anthropogenic aerosols over Europe using RegCM3. Climate Research 52, 267e278. Zhang, Y., Wen, X.Y., Jang, C.J., 2010. Simulating chemistry-aerosol-cloud-radiationclimate feedbacks over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (wrf/chem). Atmospheric Environment 44, 3568e3582.