Collaborative Project

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1 Collaborative Project Work Programme: black carbon Activity code: Coordinator: Climate Forcing of non-unfccc gases, aerosols and ENV Andreas Stohl, NILU Norsk Institutt for Luftforskning Start date of project: 1 st November 2011 Duration: 36 months Deliverable D2.2. Report on uncertainty Due date of deliverable: Project month 30 Actual submission date: Project month 33 Organisation name of lead contributor for this deliverable: FORTH Authors: Nikos Daskalakis, Stelios Myriokefalitakis, Maria Kanakidou contributors: FORTH, NILU, CICERO, METO, METOFFICE, ULEI, MET.NO

2 Brief description: Report on uncertainty estimate of seasonal source-receptor relationships, based on perturbation simulations to sectoral/regional/seasonal emissions. Evaluate how regional scale emission changes (including emission height) affect species lifetimes due to chemical ageing of air masses and atmospheric mixing and long range transport. Contents Brief description... 2 Summary... 3 Introduction Sensitivity of simulated tropospheric loading and lifetimes of short lived climate forcers and their precursors to emission perturbations over Europe... 4 Experiment setup... 4 Changes in tropospheric loads... 4 Changes in tropospheric lifetimes... 5 Methane lifetime... 5 Isoprene lifetime... 6 Black Carbon aerosol lifetime... 7 Organic Carbon aerosol lifetime... 8 Sulfate aerosol lifetime... 9 Concluding remarks Sensitivity of simulated tropospheric loading and lifetimes of short lived climate forcers and their precursors to biomass burning emissions Anthropogenic emissions Biomass burning emissions Experiment setup Global tropospheric burdens Impact of wild fires emissions Impact of injection height Chemistry feedbacks between biomass burning and vegetation emissions Tropospheric lifetimes Major findings Presentations of ECLIPSE findings Conference Presentations Conference Proceedings Peer-reviewed publications References... 20

3 Summary The impact of atmospheric pollutants on climate depends not only on the properties of the pollutant but also on its persistence in the environment that enables or limits pollutant s long range transport, reflects on its distribution in the atmosphere and is characterised by its lifetime. Therefore, this deliverable investigates the sensitivity of tropospheric lifetime and load of short lived radiative forcers and precursor atmospheric constituents to emission perturbations and to different emission inventories. For this purpose, the control simulations, the emission perturbation simulations over Europe and simulations with different biomass burning inventories and emission injection height distribution, are analysed to document the combined influence on burdens of uncertainties in coemitted species, photo-oxidant limitations, aerosol ageing and aerosol mixing state. Analysis of the impact of emission perturbations (reduction by 20%) over Europe on the lifetime of aerosol components has shown that the wintertime (or cold 6-month period) perturbation of emissions is more efficient than the summertime (or warm 6-months) perturbation in changing the lifetime of these pollutants in particular for BC and SO4 = aerosols. Thus it is expected that winter perturbations will have higher impact per emission on climate summer perturbations. The impact of NOx and NMVOC European emissions perturbations on the regional methane lifetimes maximize over Scandinavia/Finland (3-7% reduction in the regional CH4 lifetime) look almost identical for the same 6-months period indicating the equal importance of NMVOC and NOx emissions over Europe in determining the oxidizing capacity of the troposphere in this region. Biomass burning emissions inventories show large spatial, daily, seasonal and year-to-year variability. The choice of emission inventory used in a CTM is able to introduce regional differences in the calculated load of aerosols by up to a factor of 4. Assumptions on the injection height of the biomass burning emissions are found to produce regionally up to 30 % differences in the calculated tropospheric lifetimes of pollutants. Calculated changes in lifetimes have also indicated the existence of a strong chemical feedback mechanism between emissions from biomass burning and isoprene emissions from vegetation. This interaction happens via the impact of both biomass burning and vegetation emissions on OH radical levels that controls isoprene lifetime. Introduction The impact of atmospheric pollutants on climate depends not only on the properties of the pollutant but also on its persistence in the environment that enables or limits pollutant s long range transport, reflects on its distribution in the atmosphere and is characterised by its lifetime. For instance, Samset et al. (2013) have shown that black carbon has higher radiative forcing potential at higher altitudes, thus BC impact on climate depends on its lifetime. Similarly, radiative forcing is more sensitive to changes in O3 in the upper troposphere, thus upper tropospheric O3 has a stronger greenhouse effect than lower troposphere O3 (e.g. Mickley et al., 2004; Stuber et al., 2001). Therefore, this deliverable investigates the sensitivity of tropospheric

4 lifetimes and loads of short lived radiative forcers and their precursor atmospheric constituents to emission perturbations and to different emission inventories. 1- Sensitivity of simulated tropospheric loading and lifetimes of short lived climate forcers and their precursors to emission perturbations over Europe Experiment setup For this experiment, the simulations performed with a reduction of 20% in the emissions of various pollutants over Europe (T2.3) have been analyzed by comparison to the base case simulation from T2.1 with regard to the lifetime changes induced by the reduction in the individual emissions of pollutants (NOx, SO2, non-methane volatile organic compounds- NMVOC, CO, BC, OC) over source region for two periods (November to April hereafter marked as winter and May to October hereafter marked as summer ). These simulations have been performed to serve purposes of WP4 and are also discussed in detail in its deliverables. In the first section of this deliverable, we analyze the results of TM4-ECPL model for the European emissions perturbation (20% reduction) with focus on OH load and BC, OC, SO4 =, methane and isoprene lifetimes in the troposphere from surface up to about 140 hpa. The percent differences between the changes computed for the two seasonal perturbation scenaria are also depicted to demonstrate the impact of difference in the season to the impact of emission perturbation on the pollutants lifetime. The tropospheric loads (Figure 1) and lifetimes (Figures 3-6) are calculated as the mean of 12 months of simulation, starting by the 6-month perturbation period and ending by 6-month period affected by the perturbation. Overall small changes are computed in the pollutants tropospheric load and lifetime when integrated over 12- months period. This is to be expected since a small reduction of 20% has been applied. Changes in tropospheric loads The computed changes in the tropospheric load of OH radical, the driver of photochemistry and in particular of methane lifetime, are depicted in Figure 1 for the European emissions perturbation of NOx and NMVOC simulations. Fig. 1b-e (percent differences) has to be seen together with the distribution of OH tropospheric load (Fig. 1a). Thus, due to the very low OH tropospheric load at high latitudes in both hemispheres, the relatively large percent changes that are computed there have to be neglected as insignificant. This has to be kept in mind for the interpretation of all results in this deliverable. Overall, NOx summer time perturbation (reduction by 20% of European emissions of NOx during summer period) has the largest impact on OH tropospheric load over Europe leading to decreases in OH load of about 4.5%, while NMVOC 20% emission reduction over Europe during the same season has a smaller positive (increase in OH load) effect.

5 a. b. c. d. e. Figure 1. Annual mean tropospheric load of OH radical in KgOH/m 2 calculated for the base case simulation (left: a) and percent changes in the winter perturbation simulation (middle: b,d) and summer perturbation simulation (right: c,e), top (b,c) for NOx perturbation over Europe, bottom (d,e) for NMVOC perturbation over Europe. Changes in tropospheric lifetimes Methane lifetime The changes in the lifetime of methane in each of the model columns for both wintertime and summertime perturbations of European emissions are calculated (only for methane) as the difference from the base case simulations over a 6-months period following the perturbation period and are presented in Figures 2. NOx and NMVOC European emissions reductions by 20% impact on oxidant chemistry as shown in the tropospheric loads of OH radicals. Therefore they also affect the lifetimes of chemically reactive species like methane (long lived greenhouse gas) and isoprene (very reactive hydrocarbon mainly emitted by vegetation). The regional methane lifetime as calculated by the model for the base case simulation for the same 6-months periods is shown in Fig. 2a,b. The warm 6-months period in the north hemisphere from May to October is marked as summer and the cold 6-months period from November to April as winter. The consideration of 6-month periods explains the long methane lifetime calculated for the high latitudes of both hemispheres and for both 6-month periods, also keep in mind that these calculations do not account for methane losses from the respective tropospheric columns due to transport. The changes in the lifetime of methane calculated for the 6-months period following the perturbation are shown in Fig. 2c,e for the summer perturbation (20% European emissions reduction) of NOx and NMVOC respectively and Fig. 2d.f for the winter perturbation of NOx and NMVOC respectively. It is interesting to compare the impact of NOx and of NMVOC perturbations on the regional methane lifetimes. These impacts that maximize over Scandinavia/Finland (3-7% reduction in the regional CH4 lifetime) look almost identical for the

6 same 6-months period indicating the equal importance of NMVOC and NOx emissions over Europe in determining the oxidizing capacity of the troposphere. a. b. c. d. e. f. Fig. 2: Methane lifetime as calculated by TM4-ECPL model for the base case ECLIPSE simulation accounting for chemical losses in the troposphere over 6-months periods: a) Nov-April (marked as winter) and b) May-Oct (marked as summer); regional changes in winter methane lifetime compared to the base case simulation due to summertime european emissions perturbation (20% reduction) (c) of NO x, (e) of NMVOC; regional changes in summer methane lifetime compared to the base case simulation due to wintertime european emissions perturbation (20% reduction) (c) of NO x, (e) of NMVOC. Isoprene lifetime For isoprene and the aerosol components hereafter, the impact of the European emissions reductions by 20% on the lifetimes of pollutants are calculated as the percent difference from the base case simulations over a 12-months period, as earlier explained, and are presented in Figures 2-5. As expected for primary pollutants as black carbon, the emissions perturbations of the pollutant itself are those affecting most pollutants lifetime. These impacts can be seen only on regional scale, while the computed global lifetimes are not affected.

7 The impact of NOx emission reduction by 20% over Europe on isoprene lifetime (global mean isoprene lifetime in our model is calculated to be 4.4 hours) is depicted in Fig. 3. For winter perturbations decreases in the isoprene lifetime over Europe are computed, while in summer significant increases are computed over southern Scandinavia and Southern Europe. They are most probably associated with changes in O3 and NO3 radicals levels that also affect isoprene lifetime over Europe. Summertime reductions in OH tropospheric load (Fig. 1c) are in agreement with the computed increase in isoprene lifetime (up to about 12%). a. b. c. d. Fig. 3: a) isoprene lifetime as calculated for the base case ECLIPSE simulation accouting for chemical losses, b) changes in isoprene lifetime compared to the base case simulation due to wintertime perturbation of NO x european emissions (20% reduction), c) changes in isoprene lifetime compared to the base case simulation due to summertime perturbation of NO x european emissions, d) impact of NO x wintertime perturbation relative to that of summertime perturbation. Winter: Nov-April; summer: May-Oct. Black Carbon aerosol lifetime In TM4-ECPL the global lifetimes calculated for the base case ECLIPSE scenario for 2008 are 7 days for BC (Fig. 4a), 6.2 days for OC (Fig. 5a), 4.4 days for SO4 = (Fig. 6a). These values are comparable to the respective global AEROCOM median values for BC of 7.2 days and for SO4 = of 4.5 days (same forboth ExpA and expb) (Textor et al., 2007) and for OC of 7.1 days and 6.7 days (ExpA and ExpB respectively, Textor et al., 2007) and 5.4 days (range days) for AEROCOM II (Tsigaridis et al., 2014). Wintertime European BC emissions perturbation (reduction by 20%) leads to an increase in the BC lifetime (averaged over 12-months simulation as earlier explained) over Europe by 3-7% (Fig. 4b) while similar summertime perturbation leads to a decrease smaller than 3% (Fig. 4c). These results show that wintertime perturbation of BC emissions is more effective in increasing BC lifetime in the atmosphere (Fig. 4d) and thus it is expected that wintertime perturbation will have higher per emission impact on climate than summertime perturbation.

8 Organic Carbon aerosol lifetime For OC that also has a significant secondary source the European OC emissions perturbations do not show as uniform changes in OC lifetime (Fig. 5) as for BC (Fig. 4). Wintertime European OC emission perturbation (20% reduction) leads to an increase (5-10%) of OC lifetime in the Mediterranean, North Africa and Middle East, i.e. in the outflow of the European pollution, a decrease (in the range of 3-7%) at high latitudes (northern 60N) and no clear change over Central Europe (Fig. 5b). The summertime perturbation (Fig. 5c) leads to a small decrease (<3%) in OC lifetime over Europe and a slight increase (3-7%) over Northern Africa and Middle East. Again the winter perturbation is computed to lead to the largest (positive) impact on OC lifetime in particular over the Mediterranean region (Fig. 5). a. b. c. d. Fig. 4: a) BC lifetime as calculated for the base case ECLIPSE simulation accouting for deposition losses of BC, b) changes in BC lifetime compared to the base case simulation due to wintertime perturbation of BC european emissions, c) changes in BC lifetime compared to the base case simulation due to summertime perturbation of BC european emissions, d) impact of BC wintertime perturbation relative to that of summertime perturbation. a. b. Fig. 5: a) OC lifetime as calculated for the base case ECLIPSE simulation accouting for deposition losses of oc, b) changes in OC lifetime compared to the base case simulation due to wintertime perturbation of OC european emissions (20% reduction), c) changes in OC lifetime compared to the base case simulation due to summertime perturbation of OC european emissions, d) impact of OC wintertime perturbation relative to that of summertime perturbation.

9 c. d. Fig. 5-cont: a) OC lifetime as calculated for the base case ECLIPSE simulation accouting for deposition losses of oc, b) changes in OC lifetime compared to the base case simulation due to wintertime perturbation of OC european emissions (20% reduction), c) changes in OC lifetime compared to the base case simulation due to summertime perturbation of OC european emissions, d) impact of OC wintertime perturbation relative to that of summertime perturbation. Sulfate aerosol lifetime Wintertime European SO2 emissions perturbation (reduction by 20%) leads to an increase in the sulfate lifetime (averaged over 12-months simulation as earlier explained) over Europe by 3-10% (Fig. 6b) while similar summertime perturbation leads to a smaller increase of about 3-7% (Fig. 6c). These results show that wintertime perturbation of SO2 emissions is more effective in increasing SO4 = lifetime in the troposphere (Fig. 6d) and thus it is expected that wintertime perturbation will have higher per emission impact on climate than summertime perturbation. a. b. c. d. Fig. 6: a) Sulfate lifetime as calculated for the base case ECLIPSE simulation accouting for deposition losses of BC, b) changes in SO 4 = lifetime compared to the base case simulation due to wintertime perturbation of SO 2 european emissions (20% reduction), c) changes in SO 4 = lifetime compared to the base case simulation due to summertime perturbation of SO 2 european emissions, d) impact of SO 2 wintertime perturbation relative to that of summertime perturbation.

10 Concluding remarks Analysis of the impact of emission perturbations (reduction by 20%) over Europe on the lifetimes of aerosol components has shown that the wintertime (or cold 6-month period) perturbation of emissions is more efficient than the summertime (or warm 6-months) perturbation in changing the lifetimes of these pollutants in particular for BC and SO4 = aerosols. Thus it is expected that such perturbations will have higher per emission impact on climate. 2- Sensitivity of simulated tropospheric loading and lifetimes of short lived climate forcers and their precursors to biomass burning emissions The capability of CTM to simulate atmospheric composition and its spatial and temporal changes highly rely on the input data used by the models, in particular the emission inventories. Biomass burning emissions show large spatial, daily, seasonal and year-to-year variability. For this study, the global 3D CTM TM4-ECPL has been applied to evaluate uncertainties in the computed tropospheric loading and lifetime of SLP (climate active short lived pollutants) and their precursors associated with the use of different biomass burning emissions and identify areas where observational data can contribute in reducing these uncertainties. Particular attention has been paid to the agricultural waste burning (AWB) that is part of the anthropogenic contribution to the biomass burning and the associated inventories have been explicitly improved in ECLIPSE. Anthropogenic emissions Anthropogenic emissions used for this experiment are the ECLIPSE version 4.0 emissions (Klimont et al., 2013), available in 0.5 o x0.5 o spatial resolution. The ECLIPSE anthropogenic inventory was initially provided as sectoral including the agricultural waste burning sector (AWB). Since AWB is either considered to be anthropogenic or part of the biomass burning emissions, caution was taken to avoid double counting of these emissions. For this, whenever AWB was included in the biomass burning emission database (FINN, GFEDv3) used in this study, it was removed from the anthropogenic emissions, since the study is biomass burningcentered. The AWB amounts to 26.7% of the total pollutants emissions (approximately 34.5 Tg) for the year 2008 (see Table 1 for more information). Anthropogenic emissions of all basic pollutants are used (CO, nitrogen oxides (NOx), black carbon aerosol (BC), particulate organic carbon (OC), sulfur dioxide and sulfates (SOx) as well as speciated non methane volatile organic compounds (NMVOC)). Table 1 Anthropogenic emissions (Tg a -1 ) used in this study and the fraction of emissions that correspond to the AWB sector that is included in the ECLIPSE anthropogenic emissions database provided both in absolute quantities and in percentage of the total anthropogenic emissions from (Klimont et al., 2013) BC CO NOx OC SOx NMVOC ECLPSE AWB on ECLIPSE % contribution of AWB to anthropogenic

11 Biomass burning emissions A number of sensitivity simulations have been performed using different biomass burning emission inventories (Table 2). Table 2 Total annual amounts of pollutants emitted by wild fires according to the different databases used for the current work, for year 2008 in Tg.a -1. NOx is reported as NO. BC CO NOx POA SO2 NMVOC NH3 GFEDv3_ECLIPSE GFEDv FINN ACCMIP For the base simulation (S0.0), the Global Fire Emission Database v 3.1 (GFEDv3.1) (van der Werf et al., 2010) is used, excluding the AWB sector (Table 3) hereafter called GFEDv3_ECLIPSE biomass burning emissions, since it has been performed in the framework of the ECLIPSE project. Table 3 Forest fire emissions from the GFEDv3.1 inventory and the AWB fraction contained in the inventory. BC CO NOx OC SOx NMVOC GFEDv AWB in GFEDv % contribution of AWB to anthropogenic Additional simulations (Table 4) using the Atmospheric Chemistry and Climate Model Intercomparison Project s (ACCMIP) wild fire emissions (Lamarque et al., 2013), the original GFEDv3.1 (van der Werf et al., 2010) and the Fire INventory from NCAR (FINN) (Akagi et al., 2013; Akagi et al., 2011; Yokelson et al., 2013) have been performed. Table 4 Summary of simulations performed for this work. GFEDV3_ECLIPSE GFEDv3 ACCMIP FINN Height With Surface With Surface With Surface With Surface S0.0 S0.1 S1.0 S1.1 S2.0 S2.1 S3.0 S3.1 S4.0-noBB Since the injection height of these emissions contributes to the uncertainty of the model results, forest fire emissions are considered in the model either to be injected at heights following

12 recommendations by Dentener et al. (2006), or to be emitted solely at the surface (see list of simulations in Table 4). The temporal variability of theses biomass burning inventories per emitted species for 2008 is shown in Fig. 7. This figure depicts the differences between the emission inventories in their seasonality, timing and amplitude, as well as in the annual amounts emitted to the atmosphere (also in Table 2). The FINN inventory shows the largest seasonal amplitude in the temporal variation of these emissions. On the opposite, ACCMIP shows the smallest seasonality, in particular for OC, BC and NH3. The differences between GFEDv3_ECLIPSE and GFEDv3 show the seasonality and amounts of the AWB emissions (included in GFEDV3 but not GFEDV3_ECLIPSE). AWB emissions that have been excluded from the GFEDv3_ECLIPSE biomass burning database significantly contribute to NMVOC and NH3 emissions during spring and summer. Fig. 7. Biomass burning emission inventory temporal differences for year 2008 for all species used in the model. For simplicity NMVOC are summed up. Units are Tg per month.

13 Experiment setup The impact of the use of different biomass burning emission inventories to the calculated tropospheric load and lifetime of the main pollutants and the robustness of model results with regard to the biomass burning emissions have been evaluated based on nine different simulations. For all simulations the model setup was exactly the same, except for the biomass burning emissions inventory used and its vertical distribution application. A summary of the simulations here performed is provided in Table 4. The GFEDv3_ECLIPSE inventory and height distribution for biomass burning emissions have been used as the base case scenario (S0.0). All scenarios named SX.0 assume the same fractional height distribution of the emissions according to Dentener et al. (2006) where all the scenarios named SX.1 assume all open biomass burning emissions to occur at surface. For scenario S4.0, open biomass burning emissions are set to zero. Global tropospheric burdens Simulation S4.0 neglecting biomass burning emissions by comparison to the base case S0.0 simulation shows the significant impact of these emissions on pollutant global tropospheric loads (Table 5) and will be further discussed. On the opposite, changes in the respective emission inventories do not significantly impact the global tropospheric load of most pollutants (Table 5). However, regionally significant differences are computed as will be further discussed. The most sensitive pollutants to the choice of wildfire emission inventories are found to be OC and BC, while O3 tropospheric burden shows relatively small sensitivity. Table 5 Total annual mean tropospheric load of pollutants for all simulations in Tg. S0.0 S0.1 S1.0 S1.1 S2.0 S2.1 S3.0 S3.1 S4.0 CO Ozone NOx SO HNO NH Isoprene OC BC Impact of wild fires emissions. The contribution of wildfires to the tropospheric burdens of pollutants is calculated by comparison of S0.0 (base case) with S4.0 that neglects the emissions. Wildfires increase the tropospheric burdens of: OC by ~30%, BC by ~35%, CO by about 13%, NH4 + by 10%, HNO3 by 8%, NOx by 5%, SO4 2- and O3 by 3% (Table 5). OC is highly dependent on wild fires that contribute to its tropospheric annual burden by about 30%. This impact presents large temporal and spatial variability since it occurs during the burning season that lasts only a few months per year and is marked by tropical and boreal forest fires. The spatial variability of the annual mean

14 impact of wildfire emissions on the tropospheric load of OC, CO, NOx, O3, OH and isoprene is depicted in Fig. 8a-f respectively. The most affected pollutants are OC (Fig. 8a) and BC (Fig. 8g), with local reduction due to the omission of wild fires by almost 100%, while annual mean local impacts on O3 and CO with strong secondary sources maximize at 20-30% in the tropics. As expected NOx tropospheric load is mostly affected by biomass burning in the extra tropics at the outflow of the boreal fires and over the tropical regions of south America, Africa and N. Australia where burning is intensive (Fig. 8c). As a consequence of these NOx and O3 reductions, the computed hydroxyl radical (OH) load (Fig. 8e) is significantly reduced (5-10%) over the same regions with NOx. Impact of injection height The effect of height distribution of biomass burning emissions on the computed tropospheric loads has been studied by comparing the simulations SX.0 with the respective simulations SX.1. Figure 9 presents such comparisons for OC and BC. Both OC and BC are affected most by injection height assumptions, since emitting aerosols at height reduces the quantities directly deposited through dry deposition. The largest differences are computed for the high latitudes over N. America and China where the height distribution can result to about 25% differences (Fig. 9a). Differences are positive over source areas (since more is emitted near the surface) and negative downwind (since less is transported away from source regions due to the increased deposition flux there). Assumptions on the wild fire emissions injection height marginally affect CO and O3 with computed differences of the annual mean tropospheric load lower than 2.5%. Differences in computed O3 tropospheric loads are small and locally never exceed 20% between the different scenarios. Maximum differences are calculated in the tropics where most wildfire emissions are occurring under high photochemistry conditions. OH and NOx are affected more than O3 with differences reaching almost 15% locally at high latitudes. Their changes also reflect on the concentration of HNO3 where local differences reach 30% over Canada (not shown). Fig. 9 Percentage difference of annual mean computed tropospheric load of OC (a) and BC (b) attributed to wild fire emission height injection (S0.1 simulation versus S0.0). The color scale is fixed between -30% to 30%, minimum and maximum differences are printed under each panel.

15 Fig. 8 Percentage difference in the computed annual mean load of OC (a), CO (b), NOx (c), O 3 (d), OH (e), isoprene (f), black carbon (g) attributed to biomass burning emissions (comparison between simulations S4.0 and S0.0). The color scale is fixed to vary between -30% to 30%, for all panels, minimum and maximum difference are printed under each panel.

16 Chemistry feedbacks between biomass burning and vegetation emissions Very interesting results are found when examining the impact of wildfire emissions on isoprene tropospheric load. Isoprene is the single major volatile organic compound (BVOC) emitted by vegetation (more than 50% of total BVOC emissions). The above described changes in OH, the main tropospheric oxidant (Fig. 8e) that reacts with isoprene, lead to opposite in sign changes in isoprene (Fig. 8Fig. f). Such results indicate a strong chemical feedback between biomass burning and vegetation emissions. When biomass burning does not occur, up to 10 % more isoprene is calculated locally for the same isoprene emissions taken into account in the model (S4.0) due to chemistry changes. These results also show that it is critical for the evaluation of the impact of these emissions on tropospheric chemistry to consistently account for BVOC emissions from vegetation and the vicinity/co-location/co-occurrence of biomass burning emissions in the area. Tropospheric lifetimes Changes in chemistry as discussed above as well as changes in deposition of pollutants due to the modification of the spatial distribution of the pollutants affect the lifetime of these compounds in the troposphere. Thus, isoprene s lifetime is increased in S4.0, as previously explained, by almost 20%. Otherwise, global tropospheric lifetimes are less influenced by the choice of the emission inventory with a maximum of about 12% for OC. Table 6 shows the calculated global tropospheric lifetime of pollutants for each scenario. The maximum percentage differences from the base case scenario (S0.0) are computed for S4.0 simulation that neglects all wildfire emissions. Table 6 Calculated tropospheric lifetimes of pollutants for all the simulations performed. S0.0 S0.1 S1.0 S1.1 S2.0 S2.1 S3.0 S3.1 S4.0 CO (days) Ozone (days) NOx (min) SO 4 (days) HNO 3 (days) NH 4 (days) Isoprene (hours) OC (days) BC (days) Regional lifetime of pollutants computed as the ratio of the concentration to the loss rate (sum of chemical and deposition flux) in the column for each model grid, show sensitivity to both the height distribution of the injected emissions (Fig. 10 right panels) and the different emission inventories (Fig. 10 left panels). The sensitivity to the height injection of the wild fire emissions is depicted in Fig.10 b,d,f,h, where the difference in calculated tropospheric lifetimes attributed to emission injection height alone can reach 30% (for OC). The use of different wild fire emission inventories led to up to almost 90% local differences for OC where the maximum differences are computed in the tropics and over the boreal forests in Canada and eastern Russia using the ACCMIP inventory. The overall impact of wild fire

17 emissions (simulations S4.0 versus S0.0) on the regional lifetimes of tracers is shown in Fig. 11, where significant increases in CO (Fig. 11a) and Ozone (Fig. 11b) lifetimes are calculated when biomass burning emissions are neglected. Biomass burning is reducing O3 lifetime in the burning regions of the tropics and the boreal forests. Fig. 10 left panels: Impact of emission inventory choice (SX.0) to the computed tropospheric lifetime of OC as percent differences from the base case (S0.0). right panels: Impact of height distribution to the computed tropospheric lifetimes of OC as percent differences of the case SX.1 from the respective case SX.0. The color bar ranges from -30% to 30%. The minimum and maximum local lifetimes as well as the global lifetime are printed under each panel.

18 Fig. 11. Impact of wild fire emissions to the computed lifetimes of OC (a), CO (b), NOx (c), O 3 (d), BC (e) and isoprene (f) depicted as the percentage difference of S4.0 and S0.0. The color bar is fixed to range from -30% to 30%. The minimum and maximum local lifetimes as well as the global lifetime are printed under each panel. The impact on chemistry can be seen through the increases in the regional lifetime of CO and isoprene in S4.0, where local differences can reach up to 160%.

19 Tropospheric NOx lifetime strongly responds to the wild fire emissions used in the model with differences in calculated tropospheric lifetimes between about -50% and 50% (Fig. 12). Also, when wild fire emissions are omitted in the model, NOx lifetime is increasing by about 160% locally (Fig. 12d), although on global scale a smaller lifetime change is computed. Fig. 12 depicts large local differences between the different scenarios even in the sign of lifetime changes. Focusing on central Canada and eastern Russia, the S2.0 simulation results in a large increase in NOx lifetime compared to S0.0 that is not computed for the S1.0. Simulation S3.0 calculates increase only over Canada and a decrease over eastern Russia. These differences are mainly attributed to the spatial distribution of the emissions favoring different chemistry pathways and resulting in different dry and wet removal fluxes. Fig. 12- Computed tropospheric column NOx lifetime differences between the base case scenario (S0.0) and S1.0 (a), S2.0 (b), S3.0 (c) and S4.0 (d). The color bar ranges from -30% to 30%. The minimum and maximum local lifetimes as well as the global lifetime are printed under each panel. Major findings Biomass burning emissions inventories show large spatial, daily, seasonal and year-to-year variability. The choice of emission inventory used in a CTM is able to introduce differences in the calculated load of aerosols by up to a factor of 4. Assumptions on the injection height of the biomass burning emissions are found to produce regionally up to 30 % differences in the calculated tropospheric lifetimes of pollutants.

20 Calculated changes in lifetimes have also indicated the existence of a strong chemical feedback mechanism between emissions from biomass burning and isoprene emissions from vegetation. This interaction happens via the impact of both emissions on OH radical levels that controls isoprene lifetime. This feedback is shown to amplify the impact of natural emissions on tropospheric chemistry. Presentations of ECLIPSE findings Conference Presentations Daskalakis N., Myriokefalitakis S., Tsigaridis K., Kanakidou M., Global Ozone and Organic Aerosol sensitivity to biomass burning emission inventories. European Geophysical Union (EGU) 2013 General Assembly, 7-12 April, Vienna, Austria. Conference Proceedings Daskalakis N., Myriokefalitakis S., Kanakidou M.: Global Oxidant and Organic Aerosol sensitivity to biomass burning emission inventories, May 2014 COMECAP 2014, ISBN , Vol I Peer-reviewed publications Daskalakis N., Myriokefalitakis S., Kanakidou M., et al. Sensitivity of atmospheric chemistry simulations to wild fire emissions in preparation for ACPD, Tsigaridis K., Daskalakis N., Kanakidou M et al., The AeroCom evaluation and intercomparison of organic aerosol in global models, Atmos. Chem. Phys. Discuss., 14, , doi: /acpd , References Dentener, F., Kinne, S., Bond, T., Boucher, O., Cofala, J., Generoso, S., Ginoux, P., Gong, S., Hoelzemann, J. J., Ito, A., Marelli, L., Penner, J. E., Putaud, J. P., Textor, C., Schulz, M., van der Werf, G. R., and Wilson, J.: Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom, Atmos. Chem. Phys., 6, , Klimont, Z., Smith, S. J., and Cofala, J.: The last decade of global anthropogenic sulfur dioxide: emissions, Environmental Research Letters, 8, , Mickley, L. J., D. J. Jacob, B. D. Field, and D. H. Rind. Climate response to the increase in tropospheric ozone since preindustrial times: A comparison between ozone and equivalent CO 2 forcings, J. Geophys. Res., 109, D05106, doi: /2003jd003653, Samset, B.H., G. Myhre, M. Schulz, Y. Balkanski, S. Bauer, T.K. Berntsen, H. Bian, N. Bellouin, T. Diehl, R.C. Easter, S.J. Ghan, T. Iversen, S. Kinne, A. Kirkevåg, J.-F. Lamarque, G. Lin, X. Liu, J. Penner, Ø. Seland, R.B. Skeie, P. Stier, T. Takemura, K. Tsigaridis, and K. Zhang, 2013: Black carbon vertical profiles strongly affect its radiative forcing uncertainty. Atmos. Chem. Phys., 13, , doi: /acp Stuber, N., M. Ponater, and R. Sausen. Is the climate sensitivity to ozone perturbations enhanced by stratospheric water vapor feedback?, Geophys. Res. Lett., 28, , Textor, C., Schulz, M., Guibert, S., Kinne, S., Balkanski, Y., Bauer, S., Berntsen, T., Berglen, T., Boucher, O., Chin, M., Dentener, F., Diehl, T., Feichter, J., Fillmore, D., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Horowitz, L., Huang, P., Isaksen, I. S. A., Iversen, T., Kloster, S., Koch, D., Kirkevåg, A., Kristjansson, J. E., Krol, M., Lauer, A., Lamarque, J. F., Liu, X., Montanaro, V., Myhre, G., Penner, J. E., Pitari, G., Reddy, M. S., Seland, Ø., Stier, P., Takemura, T., and Tie, X.: The effect of harmonized emissions on aerosol properties in global models an AeroCom experiment, Atmos. Chem. Phys., 7, , 2007.