Sensitivity of organic aerosol simulation scheme on biogenic organic aerosol concentrations in climate projections

Organic aerosol can have important impacts on air quality and human health because of its chemical composition and its large contribution to the atmospheric fine aerosols. Simulation of this aerosol is difficult since there are many unknowns in the nature, mechanism and processes involved in the formation of these aerosols. These uncertainties become even more important in the context of a changing climate, because different mechanisms, and their representation in atmospheric models, imply different sensitivities to changes in climate variables. In this work, the effects caused by using different schemes to 5 simulate OA are explored. Three schemes are used in this work: a molecular scheme, a standard volatility basis set (VBS) scheme with anthropogenic aging and a modified VBS scheme containing functionalization, fragmentation and formation of non-volatile SOA formation for all semi-volatile organic compounds (SVOCs). 5 years of historic and 5 years of future simulations were performed using the RCP8.5 climatic scenario. The years were chosen in a way to maximize the differences between future and historic simulations. The comparisons show that for the European area, the modified VBS scheme shows 10 the highest relative change between future and historic simulations, while the molecular scheme shows the lowest (a factor of two lower). These changes are maximized over the summer period for biogenic SOA (BSOA) because the higher temperatures increase terpene and isoprene emissions, the major precursors of BSOA. This increase is partially off-set by a temperature induced shift of SVOCs to gas phase. This shift is indeed scheme dependent, and it is shown that it is the least pronounced for the modified VBS scheme including a full suite of aerosol aging processes, comprising also formation of non-volatile aerosol. 15 For the Mediterranean Sea, without BVOC emissions, the OA changes are less pronounced and, at least on an annual average, more similar between different schemes. Absolute concentrations between different schemes are also different. Our results warrant further developments in organic aerosol schemes used for air quality modelling to reduce their uncertainty, including sensitivity to climate variables (temperature).

the region for which the simulations are performed, since various areas in the world can show different sensitivity to climate change. In this study, we focus on the European continent and the Mediterranean basin. The Mediterranean basin, is one of the most sensitive regions to climate change, which makes it important and at the same time interesting to study. However, not much focus has been given to the Mediterranean in the literature, especially for the western side of this basin (Giorgi, 2006). For this reason, the ChArMEx project was put into place, in order to study the current chemical characteristics of the 15 atmosphere of the Mediterranean region and its changes in future scenarios.
In this study, future OA concentrations under a climate change scenario will be quantified using different OA schemes. Three OA simulation schemes are compared, namely (i) a two product scheme, (ii) a VBS scheme with anthropogenic aging and (iii) a modified VBS scheme including fragmentation and nonvolatile SOA formation. A representative concentration pathway climatic scenario (RCP) has been used. RCP8.5 has been chosen in order to maximize future changes and to get a clear climate 20 change related signal in our study.
The paper is organized as follow: Section 2 explains the modeling framework for this work. An evaluation of the three schemes against measurements is provided in section 3, while section 4 presents results for the different scenarios. Conclusions are presented in section 5.

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The modelling framework in this study utilizes a chain of models, covering the different compartments of the atmosphere, a global circulation model and a global chemistry transport model providing meteorological and chemical conditions of the atmosphere respectively (figure 1). In order to down-scale the output provided by the global models a regional climate model and a regional chemistry transport model are used (figure 1). Global circulation data is provided by IPSL-CM5A-MR (Taylor et al., 2012 ;Dufresne et al., 2013 ;Young et al., 2013), while the LMDZ- INCA (Hauglustaine et al., 2014) global chemistry 30 transport model, using simulations from global circulation model as meteorological input, provides boundary conditions for the regional chemistry transport model (CTM). The boundary conditions include inputs for organic carbon as well. The global circulation model also provides boundary conditions for the regional climate model, WRF (Weather Research and forecasting, Figure 1. Simulation chain used for this study: the focus of this work is the SOA scheme inside the regional chemistry transport model. Wang et al., 2015), which, in return provides meteorological input fields for the regional CTM, CHIMERE .
The WRF simulations were prepared for the EURO-CORDEX project (Jacob et al., 2014) and use representative concentration pathways (RCPs, Meinshausen et al., 2011 ;van Vuuren et al., 2011) for future simulations. Anthropogenic emissions are taken from the ECLIPSEv4a inventory (Amann et al., 2013;Klimont et al., 2013;Klimont et al., 2017), and the biogenic emissions are provided by the MEGAN model (Guenther et al., 2006). Since the focus of this article is on the SOA scheme changes in the 5 regional CTM, only this model will be discussed in further detail. More information on this modeling framework is provided in Colette et al., 2013;

CHIMERE chemistry transport model
The CHIMERE chemistry transport model has been widely used in different parts of the world (Carvalho et al., 2010;Hodzic and Jimenez, 2011), especially in Europe (Zhang et al., 2013 ;Petetin et al., 2014;Colette et al., 2015;Menut et al., 2015 ;10 Rea et al., 2015), for both forecasting and analysis purposes. It provides a wide range of capabilities; if input information such as anthropogenic/biogenic emissions, meteorological conditions are given, it can simulate an exhaustive list of atmospheric components. Different chemistry schemes are available in the model, in the case of our simulations, the MELCHIOR2 scheme (Derognat et al., 2003) is used, containing around 120 reactions. A sectional logarithmic aerosol size distribution of 10 bins is used with a range of 40nm to 40µm. The aerosol module in CHIMERE includes different chemical and physical processes such 15 as gas/particle partitioning, coagulation, nucleation, condensation, as well as dry and wet deposition. The chemical speciation contains EC (Elemental Carbon), sulfate, nitrate, ammonium, SOA/SVOC species, dust, salt and PPM (primary particulate matter other than ones mentioned above). More information on the SOA scheme will be provided in the next section. The  (upper-panel). BSOA for the June to September period was plotted against temperature (lower panel). Percentiles both for BSOA and temperature are also shown. Years with the lowest temperature and lowest BSOA concentrations for historic simulations are shown in the lower panel on the left side. Those with the highest temperature and BSOA concentrations for future scenarios are shown on the right side.

Choice of years
The SOAvbs and the SOAmod schemes are both numerically very resource-consuming, therefore, only 10 years of simulations for each scheme were performed. In order to choose the appropriate years for the simulation, an existing long-period sets of simulations were used, containing 30 years of historic simulations  and 70 years of future scenarios (2031-2100).
The simulations were performed using the previous version of CHIMERE (chimere-2013b(chimere- , Menut et al 2013, the SOA2p 5 scheme and the RCP8.5 scenario. This dataset was used to choose five years of simulations in the historical and future periods each, with the aim to maximize both the temperature and SOA differences between historic and future scenarios.   For historic simulations, the years representing the lowest temperature and BSOA concentrations are used, which correspond to years 1980, 1981, 1984, 1985 and 1986, while for future scenarios the years with the highest temperature and BSOA

Scheme validation
The three schemes show high variability when simulating the concentration and characteristics of OA, therefore, we performed an evaluation to investigate their performances. The schemes are compared to observations for the year 2013 during which an abundance of observational data is available. A year-long simulation for the year 2013 was performed for each of the schemes. The observations are mostly accessed from the EBAS database (http://ebas.nilu.no/, last accessed: ). In some cases, data 15 was provided by the lead investigator for a specific station, and the measurements for the two stations of Corsica and Mallorca have been added using the ChArMEx (http://mistrals.sedoo.fr/ChArMEx/,last accessed: ) campaign measurements. In total, 32 stations are compared to simulations. Bear in mind that for some of these stations the available data covers a shorter period than one year, or they present weekly measurements rather than daily observations. Results of these comparisons are shown in figure 3. Regarding the concentration of OA, the modified VBS scheme shows

Analysis of the simulations
The presentation of the simulations will be presented in the next two sub-sections. First, the changes in BVOC emissions are discussed. Subsequently, the results for the European continent regarding concentration, origins and oxidation state will be presented, and a general comparison of the spatial distribution will be done for different schemes. Finally, an analysis of these parameters will be performed for the Mediterranean sub-domain including their origins and the oxidation state.

Changes in biogenic emissions
The changes in biogenic emissions are important in the context of this work, since they are highly dependent to temperature changes. For the simulations presented in this work, the biogenic emissions do not change between different schemes, however they change quite a bit between historic and future simulations because of temperature increase in the future. Since the choice of the years was done to maximize future temperature changes, the differences between future and historic simulations are 10 quite remarkable. For the European region, average "historical" isoprene emissions are 1.3 × 10 11 molecules.cm 2 and average historical terpene emissions are 3 × 10 10 molecules.cm 2 . An increase of 88% and 82% for isoprene and terpenes is seen respectively in the future scenarios in response to an average temperature increase of 5.5°C. For the summer period, the biogenic emission increase raises to 93% and 92% for isoprene and terpenes for a temperature increase of 6.4°C (figure 6). The correlation between historic isoprene and terpene emissions is 0.85 and 0.6 while this correlation is 0.91 and 0.7 for the future

Changes in BSOA concentration
We address results for BSOA, as it makes the major contribution to OA during summer (between 40 and 78% for different schemes in the historic scenario). BSOA concentrations in future scenarios are predicted to increase in all the schemes. However, the intensity of this increase is scheme dependent: while for SOA2p an increase of +94% is calculated, this percentage 5 raises to +135% for SOAvbs and +189% for SOAmod. This change in intensity shows that the climate impact on changes of BSOA in the future might have been underestimated until now on a relative scale, since many of the future simulations performed to see climate impact use two-product or molecular single step schemes for the simulation of SOA, while using a VBS based scheme increases the climate induced effect on the change in BSOA concentration in the future. Reasons for this behavior will be discussed in section 5. However, we would like to notice that changes are maximized by the choice of There is a strong seasonality for the BSOA production. The seasonal changes for BSOA are seen in figure 4-a1, 4-b1 and 4-c1 for historic simulations, the absolute difference between future and historic simulations, and their relative changes.
For monthly results, as seen in figure 4-a2, 4-b2 and 4-c2 there is an increase in almost all months for all schemes during the year, but the intensity of this increase changes for different months. In July, when the BSOA concentration reaches its 10 maximum, the percentage of change in the future is high as well (+125%, +137% and +216% for SOA2p, SOAvbs and SOAmod respectively). Highest relative changes occur for august for all schemes (+133%, +168% and +333% for SOA2p, SOAvbs and SOAmod respectively). For SOAmod, a decrease is seen for some months in the future scenarios (-11%, -1.6% and -0.45% for April, October and November respectively).

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Since the schemes behave differently both in distribution of origins as well as volatility bin aspects, it is interesting to compare these two aspects in the tested schemes. In the model, the fossil/non-fossil repartition is not a direct output. However, since the surrogate species for different sources are present in the outputs, the fossil/non-fossil repartition can be easily calculated.
ASOA is in the fossil fraction (neglecting a small fraction due to bio-fuel) and BSOA in the non-fossil fraction. For carbonaceous aerosol, residential/domestic uses are considered as non-fossil as they are mostly related to wood burning (Sasser et al., 20 2012). When comparing the simulated fossil/non-fossil fraction, some differences are observed. The SOAvbs scheme predicts more in the fossil fraction mainly because it takes into account the aging of anthropogenic SVOCs and not the biogenic SVOCs.
On the other hand, the SOAmod scheme takes into account the aging for both biogenic and anthropogenic SVOCs, therefore it simulates more in the non-fossil compartment. All schemes show an increase in the contribution of non-fossil sources in the future (79%, 74% and 84% increase in non-fossil contribution for SOA2p, SOAvbs and SOAmod in future scenarios). SOA2p 25 indicates a higher increase in nonfossil contribution compared to other schemes. As already discussed, a strong seasonality is seen for this factor as well. The contribution of non-fossil sources becomes much higher in summer (figure 5), when BVOC emissions are largely abundant. The increase in the contribution of non-fossil sources is logical since the anthropogenic emissions of OA precursors that are kept the same and the biogenic emissions of these species increase with increasing temperature.
The OA oxidation state calculated for different schemes is also compared, using the definitions given to different groups of   (Paatero and Tapper, 1994), thus SOAmod will be taken here as a reference. Figure 5 shows that the predicted oxidation state of OA, is different for the three schemes. SOA2p indicates much less LVOOA and much more HOA compared to SOAmod, because POA emissions in SOA2p are considered non-volatile. SOAvbs does not form particles aged enough to be 5 considered as LVOOA, because aging of biogenic SVOCs is not taken into account. In addition, the formation of anthropogenic LVOOA is taken into account, but has a minor effect compared to the biogenic one. Thus, LVOOA is underestimated in this scheme and SVOOA overestimated. The contribution of HOA in future scenarios becomes less compared with historic simulations, probably since more BSOA formation happens in future scenarios. This is seen especially for SOA2p for which the HOA participation is more pronounced. The contribution of LVOOA becomes higher in future as well, since the volatilization 10 of this class of organic compounds is less affected by higher temperatures than that of SVOOA.  for all three scenarios (SOA2p, SOAvbs and SOAmod in first, second and third rows respectively). Third column shows the emissions of mono-terpenes and isoprene (molecules.cm -2 , first and second row) and temperature (K, third row) and the changes of each one of these parameters is seen in fourth column (%(future -historic)/historic)). Bear in mind that emissions of BVOCs and the temperature do not change between different schemes. Also, scale for each plot is different. Figure 6 shows the concentration of BSOA in different schemes (in µg.m -3 first column), the percentage of differences between historic and future simulations (second column), concentrations of isoprene and mono-terpenes and temperature for all schemes in the third column and the changes of these parameters in future scenarios in the fourth column. The concentration of BSOA in SOA2p simulations is much higher than that of SOAvbs and even more so than that in SOAmod at the lower end. However, the maximum of 70% and 200% increase for annual and summer averages respectively, for the same area. This fact might suggest that the increase of BSOA concentrations due to climate change might be highly underestimated in future scenarios. Despite these regional variations, differences between historic and future scenarios ( figure 6, second column) is similar for all schemes, showing a maximum in the band between North and Baltic Sea.

Spatial distribution of future changes
The spatial distribution of temperature increase is correlated with that of BSOA increases (for all the schemes). There is 5 an exception for the Mediterranean area, where temperatures are high, but the concentration of BSOA is low, mainly because biogenic precursors of BSOA are not emitted in this area.

Mediterranean region
While the differences between the schemes for the European area are important to explore in future scenarios, we also focus on the Mediterranean region because of several reasons: high sensitivity to climate change, high burden of OA (and PM in 10 general, Lin et al., 2012;Lin et al., 2014) and also high temperatures in the area. Because of these reasons, we perform a similar analysis as in the previous section. As explained before, a land-sea mask has been used in order to separate the Mediterranean Sea, therefore the analysis explained below regards only the Sea without any land surface cells.

Changes in BSOA concentration
There are major differences between the partitioning of PM 10 into different aerosol components over the Mediterranean area 15 compared to continental Europe. For example, the concentrations of salt and dust particles are higher, for the former because of the marine environment and for the latter because of the North African dust emissions which are transported to the Mediterranean area. On the contrary, the concentrations of nitrate and BSOA are lower than the continental area; in the case of nitrate particles, because of higher temperatures its formation is less efficient than it is in continental Europe, and for the BSOA because of lack of emission sources over the marine environment. The differences seen for BSOA concentrations in different 20 schemes is presented in figure 4 (panels 4-d, 4-e and 4-f). The behavior of different schemes in regards to differences between historic and future simulations differs between the Sea and the continental area. For BSOA changes, SOAmod still shows the largest change compared to historic simulations (72%, 73% and 81% for SOA2p, SOAvbs and SOAmod respectively), but the differences between schemes are less pronounced in the Mediterranean area.

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For all three schemes, the contribution of fossil sources to OA is slightly larger for the Mediterranean sub-domain than for Europe (figure 5). The reason for this change is the fact that there are local fossil OA formation sources in the Mediterranean Sea, i.e. shipping emissions, while OA originating from non-fossil sources are not directly emitted in this area and are transported from outside. While the contribution of non-fossil sources increases in the future scenarios, fossil sources are still the major contributors.  As seen in figure 7, there is a high correlation between BVOC emissions and temperature throughout all the seasons (shown here for summer), showing an exponential behavior with temperature. The relationship between BVOCs and temperature is reported also for the Mediterranean basin, though the emissions of these species in this area are negligible. Accordingly, the correlation is lower over this area.
When looking at the different schemes, the regression lines show some differences for the future period. Interestingly SOAmod shows a slope rather similar to that of BVOC, while slopes are lower for the SOA2p and SOAvbs. Thus for SOAmod, the temperature induced increase in BVOC fully affects BSOA. In contrast, for SOA2p and SOAvbs, less BSOA is formed with a temperature increase as could be expected from the correspondence. This negative sensitivity of BSOA formation normalized by BVOC emissions is due to a shift of SVOC species to the gas phase for increasing temperature, as has been mentioned 5 before. Apparently, this effect is much less pronounced or absent for VBSmod, probably because it includes, contrary to the other two schemes, formation of non-volatile SOA. These results suggest that the parameterization of OA schemes might lead to different sensitivity in prediction of the OA load with respect to the variations in the temperature. The same tendencies are observed for the historic period; however they show a lower intensity because of the lower general temperature ranges.

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In this study, we presented the effect of different OA simulation schemes on future aerosol projections due to climate change.
For this purpose, three schemes have been used, a molecular single-step oxidation scheme (SOA2p), a standard VBS scheme with anthropogenic SVOC aging only (SOAvbs) and a modified VBS scheme containing functionalization, fragmentation and formation of non-volatile SOA for all SVOC species (VBSmod). These schemes were evaluated for the European region for the year 2013. Although showing differences with observations, each one of OA schemes performs within accepted error ranges.

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Since VBS schemes are numerically demanding, only 10 years of simulations could be performed for each scheme. In order to maximize the differences between future and historic simulations, the RCP8.5 scenario was used. For the future scenarios, years where the temperature and the BSOA concentration were both at their maximum were chosen, while, for the historic simulations, 5 years with the lowest temperature and BSOA concentrations were selected. Indeed, climate change induced modifications were shown to affect especially the BSOA fraction of organic aerosol. 20 The results show that the change in concentration indicated by the SOAmod scheme is stronger especially for summertime, showing a difference of 122%, 149% and 244% for SOA2p, SOAvbs and SOAmod respectively, for the European area. These changes are mostly due to increased BSOA formation, which is the major SOA fraction during summer. Previous studies investigated the changes in BSOA concentrations for future scenarios using a two-product scheme for the simulation of SOA.
Thus, our suggestion is that the relative variation in SOA concentrations predicted with such schemes might be underestimated.

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The reason for the augmentation of BSOA concentrations due to climate change in future scenarios is because of the high dependency to BVOC emissions (which are major precursors of the formation of BSOA in summer/warm periods) to temperature. In a future climate, with the increase of temperatures values, the emissions of BVOCs might increase, and in our case they were predicted to increase by 88% for terpenes and 82% for isoprene (over the European domain). The effect on BSOA formation is tempered by the fact that higher temperatures favor the transition of semi-volatile organic material in the 30 gas phase. This effect is much more pronounced for SOA2p and the SOAvbs schemes than for the SOAmod scheme, which is the only scheme in our study including aging of biogenic SVOCs and the formation of non-volatile SOA. The sensitivity of the VBSmod scheme to temperature is the lowest, and its relation to BVOC emissions the most linear.
The differences were analyzed for the Mediterranean area as well, since organic aerosol and BSOA are transported to this area from continental Europe. While the concentrations in the Mediterranean and changes for future climate are lower for BSOA in general compared to the European area, the changes for this region are stronger in the VBSmod scheme as well (80%, 79% and 120% for SOA2p, SOAvbs and SOAmod respectively for summer).
In conclusion, our study suggests that the BSOA concentrations changes reported until now for future scenarios could be 5 highly uncertain, both on absolute and on relative scale. On a relative scale, the changes might be higher with OA schemes including formation of non-volatile aerosol (up to a factor of two). Future work is necessary in developing more accurate organic aerosol schemes, not only in terms of absolute concentrations simulated, but also with respect to their temperature sensitivity.