The ECHAM/MESSy Atmospheric Chemistry (EMAC) model is a numerical chemistry and climate simulation system that includes sub-models describing lower and middle atmosphere processes and their interaction with oceans, land, and human influences (Jöckel et al., 2006). EMAC includes sub-models that describe gas-phase chemistry (MECCA; Sander et al., 2011), inorganic aerosol microphysics (GMXe; Pringle et al., 2010), cloud microphysics (CLOUD; Jöckel et al., 2006), aerosol optical properties (AEROPT; Lauer et al., 2007), dry deposition and sedimentation (DRYDEP and SEDI; Kerkweg et al., 2006a), cloud scavenging (SCAV; Tost et al., 2006), emissions (ONLEM and OFFLEM; Kerkweg et al., 2006b), and organic aerosol formation and growth (ORACLE; Tsimpidi et al., 2014). The EMAC model has been extensively described and evaluated against in situ observations and satellite retrievals (Pozzer et al., 2012; Karydis et al., 2016, 2017; Tsimpidi et al., 2016b). The spectral resolution used in this study is T63L31, corresponding to a horizontal grid spacing of 1.875∘ × 1.875∘ and 31 vertical layers extending to 25 km altitude. The thickness of the first vertical layer is 68 m. The 11-year period between 2000 and 2010 is simulated, with the 1st year used as spin-up.

ORACLE is a computationally efficient sub-model for the description of OA composition and evolution in the atmosphere (Tsimpidi et al., 2014). ORACLE simulates a wide variety of semi-volatile organic products, separating them into bins of logarithmically spaced effective saturation concentrations. In this study, primary organic emissions from biomass burning and fuel combustion sources are taken into account using separate surrogate species for each source category. These surrogates are subdivided into three groups of organic compounds: LVOCs (C∗ = 10-2 µg m-3), SVOCs (C* = 100 and 102 µg m-3), and IVOCs (C* = 104 and 106 µg m-3). These organic compounds are allowed to partition between the gas and aerosol phases resulting in the formation of POA. Anthropogenic and biogenic VOCs are simulated separately, and their oxidation results in products distributed in four volatility bins with effective saturation concentrations of 100, 101, 102, and 103 µg m-3. Gas-phase photochemical reactions that modify the volatility of the organics are taken into account, and the oxidation products (SOA-sv, SOA-iv, and SOA-v) of each group of precursors (SVOCs, IVOCs, and VOCs) are simulated separately in the module to keep track of their origin. We have assumed that functionalization and fragmentation processes result in a net average decrease in volatility for SOA produced by SVOC–IVOC and anthropogenic VOC, without a net average change of volatility for SOA produced by biogenic VOCs (Murphy et al., 2012). The volatilities of SVOCs and IVOCs are reduced by a factor of 102 as a result of the OH reaction with a rate constant of 2 × 10-11 cm3 molec-1 s-1 and a 15 % increase in mass to account for two added oxygen atoms (Tsimpidi et al., 2014). LVOCs are not allowed to participate in photochemical reactions since they are already in the lowest volatility bin. In total 52 organic compounds are simulated explicitly (26 in each of the gas and aerosol phases). The model setup and the different aerosol types and chemical processes that were simulated by ORACLE in this study are illustrated in Fig. 1a. More details about ORACLE can be found in Tsimpidi et al. (2014).

The CMIP5 RCP4.5 emission inventory (Clarke et al., 2007) is used for the anthropogenic POA emissions from fuel combustion and biomass burning. The open biomass burning emissions from savanna and forest fires are based on the Global Fire Emissions Database version 3.1 (GFED v3.1; van der Werf et al., 2010). In order to convert the emitted organic carbon (OC) to organic mass (OM), OM/OC factors of 1.3 and 1.6 have been used for the anthropogenic and biomass burning emissions, respectively (Aiken et al., 2008; Canagaratna et al., 2015). Furthermore, emission fractions are used to distribute the OM to the volatility bins used by ORACLE. The sum of the emission fractions used for the volatility bins with C*≤ 104 is unity, since current emission inventories are based on samples collected at aerosol concentrations up to 104 µg m-3 (Shrivastava et al., 2008; Robinson et al., 2010). Additional emission fractions can be assigned to the volatility bins with C∗ > 104 based on dilution experiments (Robinson et al., 2007).

In this study we assume that anthropogenic fuel (fossil and biofuel) combustion emissions cover a range of volatilities from 10-2 to 106 µg m-3 and the additional IVOC emissions are 1.5 times the traditional POA emissions (Robinson et al., 2007); therefore, the sum of the emission fractions for the fuel combustion emissions is 2.5 (Fig. 2a). Biomass burning emissions are assumed to cover a range of volatilities from 10-2 to 104 (May et al., 2013a), and no additional IVOC emissions are assumed from biomass burning sources. Therefore, the sum of their emission factors is unity (Fig. 2a). Overall, the decadal average global emission flux of primary organic emissions is 44 Tg yr-1 from anthropogenic combustion sources and 28 Tg yr-1 from open biomass burning sources.

The emissions of LVOCs, SVOCs, and IVOCs are a key input for the accurate description of atmospheric OA. To quantify the sensitivity of the reference case results to the LVOC, SVOC, and IVOC emissions, three simulation tests have been designed. Figure 2 summarizes the emission factors used for the volatility distribution of the emissions, the emission rate of each volatility bin for the reference simulation, and the sensitivity tests. These are more specifically described in the following.

Low volatility: in this sensitivity simulation, we assume zero emissions of IVOCs to quantify their contribution to the formation of global SOA. Therefore, the fuel combustion and biomass burning emissions are distributed only in the LVOC (10-2 µg m-3) and SVOC (100 and 102 µg m-3) volatility bins, and the sum of their emission fractions is equal to unity (Fig. 2b). The decadal average global emission flux of primary organic emissions in this test is 18 Tg yr-1 from anthropogenic combustion sources and 28 Tg yr-1 from open biomass burning sources (Table 1).

High IVOCs: to estimate an upper limit of the IVOC contribution to the formation of SOA, a sensitivity simulation is conducted in which the emissions of IVOCs are increased by 1.5 times the original POA emissions. These emissions are distributed in the volatility bins with C∗ of 104 and 106 µg m-3 (Fig. 2c) by applying an additional emission factor of 0.5 and 1, respectively. The LVOC and SVOC emissions are the same as in the reference simulation. Overall, the total anthropogenic and biomass burning emissions are 4 and 2.5 times higher, respectively, than the original POA emission inventory. The decadal average global emission flux of primary organic emissions in this sensitivity test is 71 Tg yr-1 for both anthropogenic and open biomass burning sources (Table 1).

Alternative POA emissions: to investigate the sensitivity of the model results to the magnitude of the POA emissions, we have utilized the AEROCOM database for the POA emissions from anthropogenic combustion sources (Dentener et al., 2006) and the CMIP5 RCP4.5 emission inventory for the POA emissions from open biomass burning sources. These emission inventories include 36 % lower POA emissions from anthropogenic combustion sources and 33 % higher POA emissions from open biomass burning sources on average over the 2000–2010 decade compared to the reference simulation. The assumed volatility distributions are the same as in the reference simulation. The decadal average global emission flux of primary organic emissions in this case is 29 Tg yr-1 from anthropogenic combustion sources and 38 Tg yr-1 from open biomass burning sources (Table 1).

The photooxidation of SVOCs and IVOCs emitted from fuel combustion and biomass burning sources can lead to the formation of substantial SOA mass on a global scale (Jathar et al., 2011; Tsimpidi et al., 2014). To evaluate the sensitivity of the model to the parameters used to describe the aging process, we have conducted three sensitivity simulations described below.

High reaction rate constant: in this simulation we investigate the sensitivity of the results to the rate constant used for the gas-phase photooxidation of SVOCs and IVOCs with OH. We assume that the corresponding oxidation rate constant is twice that of the reference simulation and equal to 4 × 10-11 cm3 molec-1 s-1. All other parameters remained the same as in the reference simulation (Table 1).

Alternative aging scheme: to quantify the sensitivity of the results to the aging scheme, we designed a sensitivity case in which the aging scheme of Robinson et al. (2007) is used (Fig. 1b). Based on this implementation, we are using nine volatility bins (compared to five in the reference simulation) to distribute the primary emissions into LVOCs (10-2 and 10-1 µg m-3), SVOCs (100, 101, and 102 µg m-3), and IVOCs (103, 104, 105, and 106 µg m-3). This model set up is based on the formulation proposed by Shrivastava et al. (2008). The volatility distribution of anthropogenic combustion and open biomass burning emissions is shown in Fig. 2d. The sum of these emission factors is the same as in the reference simulation (2.5 for fuel combustion and 1 for biomass burning). However, the relative importance of SVOCs and IVOCs to total OA emissions is changed compared to the reference simulation. In the sensitivity simulation the fraction of SVOCs to the total emissions is 20 % for fOA (anthropogenic OA from fossil fuel and biofuel combustion) and 60 % for bbOA (natural OA form open biomass burning; Fig. 2d), compared to 32 and 70 %, respectively, in the reference simulation (Fig. 2a). Furthermore, the saturation concentration of the organic vapors reacting with OH is reduced by a factor of 10 (instead of 100 in the reference simulation), with a rate constant of 4 × 10-11 cm3 molec-1 (double the value used in the reference simulation) and a 7.5 % increase in mass to account for one added oxygen (half the value used in the reference simulation). The formation of SOA from LVOCs is possible in this configuration (contrary to the reference simulation) due to the presence of two species in the LVOC volatility range (C∗ < 3.2 × 10-1 µg m-3). Overall, in this simulation, 46 surrogate OA species are used to track the source- and volatility-resolved OA components compared to 26 aerosol species in the reference simulation.

Hybrid aging scheme: the reference and alternative aging scheme simulations assume that the volatility of the organic vapor precursors is reduced by 2 and 1 orders of magnitude, respectively, after each oxidation step. However, photooxidation reactions of IVOCs can create products with a volatility 1 to 4 orders of magnitude lower (Kroll and Seinfeld, 2008). Furthermore, recent experiments indicate that the reduction in volatility due to oxidation reactions changes as the organic molecules become more oxygenated and fragmentation becomes important (Chacon-Madrid et al., 2013). To investigate the effect of these assumptions on the predicted global SOA burden, we have modified the OA chemistry mechanism to include a hybrid method to calculate the SOA formation from the oxidation of IVOCs based on the approach of Jathar et al. (2012). The SVOC oxidation scheme remains the same as in the reference simulation. The hybrid aging scheme distributes the IVOC first generation oxidation products over a range of volatilities, with larger reductions in volatility compared to the reference simulation. The oxidation of each IVOC is assumed to result in the formation of two condensable organic gases with 4 and 6 orders of magnitude lower volatility and aerosol yields equal to 0.71 and 0.115, respectively (Jathar et al., 2014) (Fig. 1c). Then, the reference oxidation scheme is used for subsequent oxidation of these products assuming a factor of 100 reduction in volatility with a 15 % increase in mass. The photooxidation of SVOCs and IVOCs in the hybrid aging scheme is described by the following reactions: SVOCi+OH→1.15SOG-svi-1,SOG-svi+OH→1.15SOG-svi-1,SOG-svi↔SOA-svi,IVOCi+OH→0.71SOG-ivi-2+0.115SOG-ivi-3,SOG-ivi+OH→1.15SOG-ivi-1,SOG-ivi↔SOA-ivi, where i is the original volatility bin and i-1, i-2, and i-3, are the volatility bins with saturation concentrations reduced by a factor of 102, 104, and 106, respectively. The term SOG corresponds to secondary organic gas. This representation is more consistent with SOA formation from VOCs and provides in principle at least a more realistic representation of SOA formation from IVOCs.

The wet and dry removal of the organic vapors from the atmosphere depends on their ability to partition into water which is commonly expressed by their Henry's law constant (H). Two sensitivity simulations where performed to investigate the effect of this uncertain parameter.

Low solubility: to test the sensitivity of the results to the solubility of the SVOC and IVOC vapors, we have conducted a simulation using a Henry's law constant 2 orders of magnitude lower than the reference simulation and equal to 103 M atm-1 for both primary and secondary SVOCs and IVOCs.

Variable solubility: the photochemical aging of organic vapors results on average in less volatile and more hydrophilic products (Jimenez et al., 2009). To quantify the effect of this change on the model results, we have conducted a sensitivity simulation in which the fresh SVOCs and IVOCs are hydrophobic with H=10 M atm-1 and become more hydrophilic after their photochemical oxidation with an H=105 M atm-1.

In the first sensitivity test, the IVOC emissions are set to zero and only semi-volatile organic compounds are emitted. This is accompanied by an increase in SVOC emissions from anthropogenic and open biomass burning sources by 100 and 40 %, respectively. This initial partitioning of the emissions favors the particulate phase, resulting in an increase in POA compared to the reference scenario (Fig. 4a). The largest fPOA and bbPOA increases are predicted over eastern China (4.3 µg m-3) and the Congo Basin (3.9 µg m-3), respectively. The higher SVOC emissions in the sensitivity simulation result in an increase in the simulated SOA-sv concentrations as well (Fig. 5a). However, since a large fraction of the emitted SVOCs remains in the particle phase, the SOA-sv concentration increase is smaller than the corresponding changes in POA. Relatively strong fSOA-sv and bbSOA-sv increases are found over the Indo-Gangetic Plane (IGP) (0.4 µg m-3) and the Congo Basin (1.3 µg m-3), respectively. The “low-volatility” simulation does not predict any SOA-iv as it assumes zero IVOC emissions. Therefore, SOA-iv concentrations are zero around the globe, resulting in substantial decreases in areas where the reference simulation predicts high SOA-iv levels (Figs. 3d, c and 6a).

The significant decrease in organic emissions from anthropogenic sources (Table 1) due to the lack of IVOC emissions results in an overall decrease in total OA concentrations by up to 5 µg m-3 over anthropogenically polluted regions (Fig. 7a). On the other hand, organic emissions from open biomass burning sources remain at the same level as the reference simulation (Table 1); however, they are assumed to have lower volatility. This results in an increase in total OA concentrations in the sensitivity simulation by up to 2 µg m-3 over the tropical and boreal forests. Overall, the calculated tropospheric burden of POA in the sensitivity simulation increases by around 50 % due to the increase in the SVOC emissions (Table 2). For the same reason, the tropospheric fSOA-sv and bbSOA-sv burdens increase by 14 and 39 %, respectively. Nevertheless, the absence of IVOC emissions, and thus the significant decrease in anthropogenic organic compound emissions, results in a decrease in the total OA tropospheric burden by 23 %. This result emphasizes the importance of the volatility distributions used in the simulation and the contribution of IVOC emissions to SOA formation on a global scale.

The simulated POA in the reference model configuration is very close to the average HOA concentrations derived from the AMS measurements (Table 3). Therefore, assuming lower volatility of the organic emissions results in overprediction (NMB = 43 %). However, the performance of the model is significantly improved during winter (Fig. 8), since POA concentrations during that season were underpredicted (NMB = -37 %; Tsimpidi et al., 2016). On the other hand, during spring the overestimate of POA increases in the sensitivity simulation (NMB = 86 %) compared to the reference simulation (NMB = 26 %). For summer and autumn, the performance of the model changes from a slight underestimation of POA in the reference simulation (NMB = -15 %) to a slight overprediction in the sensitivity test (NMB = 30 %). The performance of the model in reproducing the OOA concentrations worsens in this sensitivity simulation (Table 4). OOA was underpredicted by the model reference simulation (NMB = -31 %); therefore, neglecting SOA formation from IVOC emissions in the sensitivity run results in an even larger OOA underestimation (NMB = -52 %). The performance of the model does not change significantly during winter (Fig. 8) since the simulated SOA formation during this season is low (Tsimpidi et al., 2016). The highest change in model performance occurs during spring when SOA is predicted to reach the annual maximum (Tsimpidi et al., 2016); the predicted underestimation of OOA increases from 20 % in the reference simulation to 50 % in the sensitivity simulation. These results indicate that the omission of IVOCs as a source of SOA in atmospheric models can result in a significant underestimation of OA concentrations, especially during periods where formation of SOA is strong.

In the second sensitivity simulation, the increased IVOC emissions result in an increase in total organics by 60 % and 150 % from anthropogenic and open biomass burning sources, respectively (Table 1). These additional organic emissions are distributed only in the intermediate-volatility bins; therefore, their impact on the simulated POA and SOA-sv levels is marginal (Figs. 4b and 5b, respectively). POA increases up to 0.6 µg m-3 over eastern China, while SOA-sv decreases up to 0.3 µg m-3 over the Congo Basin. This effect can be explained by the assumption that SOA-sv and SOA-iv form a pseudo-ideal solution. As a result, the increased SOA-iv concentrations calculated in the sensitivity simulation favor the partitioning of the fresh SVOCs into the aerosol phase, forming additional POA. At the same time, SVOCs decrease in the gas phase and therefore the formation of SOA-sv is reduced in the sensitivity simulation. As expected, the largest effect is found for SOA-iv (Fig. 6b). The significant increase in IVOC emissions results in large changes of SOA-iv over areas close to anthropogenic sources (up to 5.7 µg m-3 over the IGP) and biomass burning regions (up to 5.3 µg m-3 over the Congo Basin). The increase in SOA-iv dominates the effect on total OA concentrations that increase up to 6 µg m-3 (Fig. 7b). Overall, the predicted changes of the tropospheric burden of POA and SOA-sv are small (Table 2). However, the tropospheric burdens of fSOA-iv and bbSOA-iv increase by 88 and 115 %, respectively, resulting in an increase in the total OA burden by 38 %.

The additional IVOC emissions assumed in this sensitivity test do not affect the performance of the model for POA. On the other hand, these additional emissions bring the predicted SOA concentrations closer to the measured OOA levels (Table 4; Fig. 8). The NMB improves from -31 % in the reference simulation to -10 %. With the exception of winter, where the model still underpredicts OOA levels (MB = 2.2 µg m-3, Fig. 8), the performance of the model for SOA improves, with seasonal NMB ranging from -16 (during summer) to 11 % (during spring), compared to -33 and -20 % for the reference model, respectively. The improved performance of the model due to the increase in IVOC emissions supports the hypothesis that the IVOC emissions may have been underestimated in previous modeling studies that assumed IVOC / POA = 1.5 (Ots et al., 2016).

The final emission sensitivity test is used to estimate the uncertainty introduced by the choice of emission database. The inventories used in the sensitivity simulation assume 36 % lower fuel combustion OA emissions and 33 % higher biomass burning OA emissions compared to the reference simulation, while the total OA emissions are only reduced by 9 %. Since the volatility distribution of the emissions is identical to the reference simulation, the fractional changes of the calculated POA, SOA-sv, and SOA-iv are also similar (Table 4). The tropospheric burden of fOA (the sum of fPOA, fSOA-sv, and fSOA-iv) decreases by 34 %. On the other hand, bbOA (the sum of bbPOA, bbSOA-sv, and bbSOA-iv) increases by 11 %. Overall, the total tropospheric OA burden increases by only 4 %. The changes in fOA and bbOA concentrations, however, are not spatially uniform. Over Europe, fOA decreases everywhere, up to 3.3 µg m-3, except in Paris where fOA increases by 0.24 µg m-3. Over the US fOA slightly increases (mostly over the northeast by up to 0.6 µg m-3), while it decreases over Mexico by as much as 1.7 µg m-3. The largest fOA change is predicted over Asia, where fOA decreases significantly, up to 8.3 µg m-3, mostly over East Asia and the IGP. Here, bbOA decreases over the boreal forests (up to 3.6 µg m-3), while it increases significantly over the Southeast Asia tropical forests by up to 14 µg m-3. Over the Amazon and Congo forests, bbOA concentrations change significantly (the bbOA changes vary from -2.4 to 3.3 µg m-3 in the Amazon and from -5.3 to 7.8 µg m-3 in Congo), but the average bbOA concentration over both regions remains the same. Overall, the fOA and bbOA emission changes lead to total OA increases over the tropical and boreal forests and decreases over anthropogenic areas (Fig. 7c).

The lower OA emissions used in the sensitivity simulation (especially over China and Europe) result in a reduction of both total POA and SOA concentrations (Tables 2 and 3). Consequently, the model now underestimates POA with NMB = -25 and SOA with NMB = -40 %. These results suggest that the use of the CMIP5 RCP4.5 emission inventory in EMAC results in OA concentrations that agree more closely with the measurements compared to the AEROCOM database. It also underscores the large uncertainty associated with primary OA emissions.

In this sensitivity simulation, the photochemical reaction rate constant for SVOCs and IVOCs has been doubled compared to the reference simulation. This results in an increase in SOA-sv and SOA-iv concentrations worldwide (Figs. 5d and 6d). SOA-sv increases, by up to 0.65 µg m-3, mostly over the tropics and the polluted regions of eastern China and the IGP (Fig. 5d). The effect on SOA-iv concentrations is even more significant, since IVOCs undergo more oxidation steps before forming SOA than SVOCs. SOA-iv increased by up to 2.4 µg m-3 mostly over the IGP and eastern China (Fig. 6d). The SOA-iv increase over the tropics is smaller (up to 0.8 µg m-3) due to the assumed low fraction of IVOCs in biomass burning emissions. Overall, the tropospheric burdens of SOA-sv and SOA-iv both increase by 0.04 Tg (or 11 and 7 %, respectively). POA is not expected to be affected directly by the change of the reaction rate constant. However, the substantial reduction of gas-phase SVOCs (due to their increased reactivity) results in the re-evaporation of POA to achieve equilibrium, reducing its concentration (Fig. 4d) mainly over the tropics (up to 0.21 µg m-3). This results in an overall decrease in the tropospheric POA burden by 8 %. Following the significant increase in both SOA-sv and SOA-iv, total OA increases worldwide by up to 3 µg m-3 (Fig. 7d). Overall, the tropospheric burden of total OA increases by 4 %.

The model performance for POA is not affected by the change of the reaction rate constant (Table 2), since POA remains largely unchanged over the Northern Hemisphere (Fig. 4d). On the other hand, the performance of the model regarding SOA is significantly improved (Table 3). The underestimation of SOA by the model is reduced (NMB = -22 %) compared to the reference simulation (NMB = -31 %). The best performance is found during spring (NMB = -7 %) when the calculated SOA is almost unbiased. However, during winter, the model still severely underestimates SOA (NMB = -77 %), which indicates that the gas-phase oxidation of SVOCs and IVOCs does not suffice to explain the underprediction of SOA in winter.

In this sensitivity simulation we used the chemical aging scheme of Robinson et al. (2007), which is currently the most commonly used in VBS models. This aging scheme is accompanied by changes in the number of volatility bins used and the assigned emission factors, the oxidation rate constant, the volatility reductions after each oxidation step, and the increase in mass due to added oxygen (as discussed in Sect. 3.2). The changes in the number of volatility bins and the emission factors used for the SVOCs (Fig. 2d) result in reduced condensation of SVOCs into the particulate phase during the initial partitioning and therefore in a significant decrease in POA (Fig. 4e). The decrease in POA is global and most prominent over eastern China (up to 9.3 µg m-3). This reflects a significant change in the tropospheric burdens of both fPOA and bbPOA by 65 and 38 %, respectively.

Furthermore, the reduced fraction of SVOCs to total OA emissions (see Sect. 3.2) results in a worldwide decrease in SOA-sv (Fig. 5e) and an increase in SOA-iv (Fig. 6e). SOA-sv decreases up to 1.8 µg m-3 over the Congo Basin and the IGP. Similar to POA, the tropospheric burden of fSOA-sv and bbSOA-sv decreases by 68 and 47 %, respectively. On the other hand, the increase in SOA-iv, due to the increase in the IVOC fraction of the emissions, is not as strong as the decrease in SOA-sv (Table 4). This is due to the slower aging in the sensitivity simulation (Fig. 1b), compared to the reference simulation (Fig. 1a), which limits the formation of SOA from IVOCs. SOA-iv increases up to 0.9 µg m-3 over the Congo Basin and the IGP, while it locally decreases by 0.1 µg m-3 over Beijing, for example. The tropospheric burden of fSOA-iv and bbSOA-iv increases by 14 and 30 %, respectively. Overall, the sum of SOA-sv and SOA-iv decreases by 7 % due to the slower aging in this sensitivity simulation. Following the simultaneous decrease in both POA and SOA, total OA decreases worldwide by up to 11 µg m-3 (Fig. 7e), and its tropospheric burden is reduced by 0.2 Tg (or 10 %).

The reduction of both modeled POA and SOA results in reduced agreement of the model with AMS measurements. For POA, especially, the modeled concentrations decrease by 67 % in the sensitivity simulation, resulting in a significant underprediction of AMS–HOA (NMB = -67 %). Modeled SOA also decreases (by 10 %) in the sensitivity simulation, which degrades the model agreement with AMS–OOA measurements (NMB = -38 %). This sensitivity test underscores the significance of the volatility distribution of the organic emissions and the associated aging scheme.

The final chemistry sensitivity simulation focuses on the photochemical aging of IVOCs and assumptions regarding the first oxidation step. The approach used here is similar to the oxidation of the traditional VOCs, in contrast with the reference simulation where the oxidation of IVOCs produces only one product with a 2 orders of magnitude reduced volatility. However, the stoichiometric coefficient used in the reference simulation (equal to 1.15) is higher than the aerosol yields used in the sensitivity simulation (Sect. 3.2). This results in a reduction of SOA-iv concentrations by up to 2.2 µg m-3 (Fig. 6f). Since the chemical scheme for SVOCs is identical in both the reference and the sensitivity simulations, no significant change is found in either SOA-sv or POA (Figs. 5f and 4f, respectively). The decrease in SOA-iv concentrations has a marginal effect on the initial partitioning of SVOC emissions, resulting in slightly less POA and more SOA-sv (by up to 0.1 µg m-3 in either case). Therefore, total OA concentrations are reduced worldwide following the decrease in SOA-iv. Overall, the tropospheric burden of SOA-iv decreases by 37 % in the sensitivity simulation, resulting in a decrease in total OA by 13 % (Table 4).

The simulated POA concentrations remain almost unchanged in the sensitivity simulation; therefore, similar to the reference simulation, the calculated POA is unbiased compared to measurements (Table 2). On the other hand, the lower SOA-iv concentrations calculated by the model in this sensitivity test aggravate the underestimation of OOA by the model (NMB = -39 %). The decrease in modeled SOA-iv concentrations is larger during spring (13 %), and the calculated NMB for SOA deteriorates from -20 % in the reference simulation to -30 % in the sensitivity simulation.

In this sensitivity test we used a Henry's law constant that is 2 orders of magnitude lower than in the reference simulation (see Sect. 3.3) for the gas-phase SVOCs and IVOCs. This change decreases their removal rate, thus increasing their lifetime and the concentrations of both POA (due to the condensation of the fresh SVOCs) and SOA (due to the condensation of the chemically aged SVOCs and IVOCs). POA increases up to 0.7 µg m-3 over eastern China (Fig. 4g) where POA concentrations are relatively high (Fig. 3b); however, the increase in POA in the rest of the world is less than 0.2 µg m-3 (Fig. 4g). SOA-sv increases up to 0.2 µg m-3 mostly over the Congo Basin and the IGP (Fig. 5g). The most significant change is calculated for SOA-iv. SOA-iv is formed from gases (i.e., IVOCs) that need to go through more than two oxidation steps to be able to condense to the aerosol phase (in comparison to only one oxidation step for SVOCs). Therefore, by lowering the Henry's law constant of IVOCs we prolong the lifetime of SOA-iv precursors and their ability to undergo multiple oxidation steps and produce aerosols. This results in a significant increase in SOA-iv by up to 1.2 µg m-3 (Fig. 6g). Total OA increases by up to 2 µg m-3 due to the simultaneous increase in both POA and SOA (Fig. 7g). Overall, the tropospheric burden of SOA-iv increases by 17 % and that of total OA by 8 %. It is also worth noting that the tropospheric burden of fOA (sum of fPOA, fSOA-sv, and fSOA-iv) increases by 18 % compared to an increase of 5 % of the bbOA (sum of bbPOA, bbSOA-sv, and bbSOA-iv). The above results emphasize the significance of the removal of organic vapors for the calculated OA concentrations and corroborate the importance of constraining the Henry' law constants of SVOCs and more importantly of IVOCs.

The change in the Henry's law constant of SVOCs does not affect the model performance for POA significantly. POA slightly increases (by 4 %), eliminating the already low model bias (Table 2). The SOA increase (by 12 %) in the sensitivity simulation (mainly due to the increased SOA-iv) results in reduced SOA underestimation (Table 2). In both POA and SOA cases the effect is more important during winter, when wet removal is most efficient, and lower during summer. POA increases during winter by 10 % while during summer it remains unchanged. SOA increases during winter by 26 % and during summer by only 3 %, with spring and autumn in between (∼ 12 %). Despite the wintertime POA and SOA increase in this sensitivity simulation, the model still underestimates POA (NMB = -31 %) and SOA (NMB = -78 %) during this season (Fig. 8).

In the last sensitivity test we assume that the freshly emitted SVOCs and IVOCs are hydrophobic (with the Henry's law constant H being 4 orders of magnitude lower than the reference), while after photochemical aging H increases to match the value used in the reference simulation (see Sect. 3.3). POA increases up to 0.7 µg m-3, mostly over eastern China and to a lesser degree over eastern Europe and Russia (Fig. 4h). SOA-sv increases up to 0.2 µg m-3, mostly over the tropical forests of Central Africa and southeastern Asia as well as over eastern China and the IGP (Fig. 5h). SOA-iv also increases by up to 1 µg m-3 µ(Fig. 6h) because fresh IVOCs are more hydrophobic in the sensitivity simulation; therefore, the time available to react with OH is extended, forming additional SOA-iv. Total OA concentrations increase by up to 2 µg m-3 over eastern China (Fig. 7h). The tropospheric burden of total OA increases by 8 % in this sensitivity test with the strongest increase coming from fSOA-iv (21 %).

Both the predicted POA and SOA increase in the sensitivity simulation by 6 and 12 %, respectively. This results in a small overprediction of POA (NMB = 4 %), compared to a small underprediction in the reference simulation (NMB = -3 %). For SOA, NMB improves in the sensitivity simulation (NMB = -23 %) compared to the reference simulation (-31 %). Similar to the previous sensitivity test (Sect. 7.1), the effect is more relevant during winter (POA and SOA increase by 9 and 36 %, respectively), followed by spring (POA and SOA increase by 8 and 16 %, respectively) and autumn (POA and SOA increase by 7 and 10 %, respectively), and is small during summer (POA and SOA increase by 2 and 5 %, respectively; Figs. 8). This results in an improved model performance for both POA and SOA during all seasons. The highest improvement is found for SOA during spring when the NMB is reduced to -6 % from -20 % in the reference simulation. Despite the significant increase in SOA concentrations during winter (by 36 %), the model still strongly underestimates SOA (NMB = -76 %), indicating that the model underprediction of OOA cannot be attributed solely to errors in the simulation of removal processes. Therefore, we expect that the discrepancy in this season is related to sources that are missing or underestimated in emission inventories (e.g., residential wood combustion in winter, Denier van der Gon et al., 2015), to additional oxidation pathways (e.g., aqueous-phase and heterogeneous oxidation reactions), and to uncertainties in SOA yields due to wall losses in laboratory chambers.