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ERROR: type should be string, got "https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. The global impact of the transport sectors on aerosol and climate\nunder the Shared Socioeconomic Pathways (SSPs)\nMattia Righi, Johannes Hendricks, and Sabine Brinkop\nDeutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany\nCorrespondence: Mattia Righi (mattia.righi@dlr.de) The global impact of the transport sectors on aeroso\nunder the Shared Socioeconomic Pathways (SSPs) The transport-induced impacts on aerosol mass and number concentrations result in a present-day radiative forc-\ning of −164, −145 and −64 mW m−2, for land transport, shipping and aviation, respectively, with a dominating contribution\nby aerosol-cloud interactions. These forcings represent a marked offset to the CO2 warming from the transport sectors and are therefore very relevant for climate policy. The projections under the SSPs show that the impact of the transport sectors on\n15\naerosol and climate are generally consistent with the narratives underlying these scenarios: the lowest impacts of transport on\nboth aerosol and climate are simulated under SSP1, especially for the land transport sector, while SSP3 is generally character-\nized by the largest effects. Notable exceptions to this picture, however, exist, as the emissions of other anthropogenic sectors\nalso contribute to the overall aerosol concentrations and thus modulate the relevance of the transport sectors in the different\nscenarios, not always consistently with their underlying storyline. On a qualitative level, the results for present-day mostly\n20\nconfirm the findings of our previous assessment for the year 2000, which used a predecessor version of the same model and are therefore very relevant for climate policy. The projections under the SSPs show that the impact of the transport sectors on\n15\naerosol and climate are generally consistent with the narratives underlying these scenarios: the lowest impacts of transport on\nboth aerosol and climate are simulated under SSP1, especially for the land transport sector, while SSP3 is generally character-\nized by the largest effects. Notable exceptions to this picture, however, exist, as the emissions of other anthropogenic sectors\nalso contribute to the overall aerosol concentrations and thus modulate the relevance of the transport sectors in the different are therefore very relevant for climate policy. The projections under the SSPs show that the impact of the transport sectors on\n15\naerosol and climate are generally consistent with the narratives underlying these scenarios: the lowest impacts of transport on\nboth aerosol and climate are simulated under SSP1, especially for the land transport sector, while SSP3 is generally character-\nized by the largest effects. Notable exceptions to this picture, however, exist, as the emissions of other anthropogenic sectors\nalso contribute to the overall aerosol concentrations and thus modulate the relevance of the transport sectors in the different scenarios, not always consistently with their underlying storyline. The global impact of the transport sectors on aeroso\nunder the Shared Socioeconomic Pathways (SSPs) Abstract. A global aerosol-climate model is applied to quantify the impact of the transport sectors (land transport, shipping\nand aviation) on aerosol and climate. Global simulations are performed for present-day (2015), based on the emission inventory\nof the Climate Model Intercomparison Project Phase 6 (CMIP6), and for near-term (2030) and mid-term (2050) future projec-\ntions, under the Shared Socioeconomic Pathways (SSPs). The results for present-day show that land transport emissions have a large impact on near-surface concentrations of black carbon and aerosol nitrate over the most populated areas of the globe,\n5\nbut with contrasting patterns in terms of relative contributions between developed and developing countries. In spite of the\nrecently introduced regulations to limit the fuel sulphur content in the shipping sector, shipping emissions are still responsible\nfor a considerable impact on aerosol sulfate near-surface concentrations, about 0.5 to 1 µg m−3 in the most travelled regions,\nwith significant effects also on continental air pollution and in the northern polar regions. Aviation impacts on aerosol mass a large impact on near-surface concentrations of black carbon and aerosol nitrate over the most populated areas of the globe,\n5\nbut with contrasting patterns in terms of relative contributions between developed and developing countries. In spite of the\nrecently introduced regulations to limit the fuel sulphur content in the shipping sector, shipping emissions are still responsible\nfor a considerable impact on aerosol sulfate near-surface concentrations, about 0.5 to 1 µg m−3 in the most travelled regions,\nwith significant effects also on continental air pollution and in the northern polar regions. Aviation impacts on aerosol mass are found to be quite small, of the order of a few nanograms per cubic meter, while this sector considerably affects particle\n10\nnumber concentrations, contributing up to 20–30% of the upper tropospheric particle number concentration at the northern\nmid-latitudes. The transport-induced impacts on aerosol mass and number concentrations result in a present-day radiative forc-\ning of −164, −145 and −64 mW m−2, for land transport, shipping and aviation, respectively, with a dominating contribution\nby aerosol-cloud interactions. These forcings represent a marked offset to the CO2 warming from the transport sectors and are found to be quite small, of the order of a few nanograms per cubic meter, while this sector considerably affects particle\n10\nnumber concentrations, contributing up to 20–30% of the upper tropospheric particle number concentration at the northern\nmid-latitudes. The global impact of the transport sectors on aeroso\nunder the Shared Socioeconomic Pathways (SSPs) On a qualitative level, the results for present-day mostly\n20\nconfirm the findings of our previous assessment for the year 2000, which used a predecessor version of the same model and\nthe CMIP5 emissions data. Some important quantitative differences are found, which can mostly be ascribed to the improved\nrepresentation of aerosol background concentrations in the present study. scenarios, not always consistently with their underlying storyline. On a qualitative level, the results for present-day mostly\n20\nconfirm the findings of our previous assessment for the year 2000, which used a predecessor version of the same model and\nthe CMIP5 emissions data. Some important quantitative differences are found, which can mostly be ascribed to the improved\nrepresentation of aerosol background concentrations in the present study. 1 1 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 1\nIntroduction These compounds have\na detrimental effect on the air quality, while some of them can also impact climate (Fiore et al., 2012; von Schneidemesser Although the climate policies mostly target the emissions of CO2 and other greenhouse gases, the combustion processes\n35\npowering vehicles, ships and aircraft are also responsible for the emissions of several types of short-lived pollutants, such as\nnitrogen oxides (NOx=NO+NO2), carbon monoxide (CO), non-methane hydrocarbons (NMHC), sulphur dioxide (SO2) and\naerosol particles in form of black carbon (BC), organic carbon (OC) and primary aerosol sulfate (SO4). These compounds have\na detrimental effect on the air quality, while some of them can also impact climate (Fiore et al., 2012; von Schneidemesser et al., 2015). For example, NOx and NMHC are precursors of ozone, which is a well-known greenhouse gas (e.g. Stevenson\n40\net al., 2013; Mertens et al., 2018), while aerosol and their precursors can alter the planetary radiation balance via their direct\ninteraction with solar radiation (absorption and scattering, leading to warming and cooling, respectively; Boucher et al., 2013). Perhaps most importantly, aerosol can further impact the climate by interacting with clouds and modifying their radiative prop-\nerties, resulting in an overall cooling effect (Bellouin et al., 2020). Air pollution reduction measures targeting these compounds may therefore affect both air quality and climate, with beneficial effects for both in some cases (e.g. when targeting BC, which\n45\nhas a warming effect on climate and a detrimental impact on air quality; Bond et al., 2013), and trade-offs in other cases, like\nsulphur reduction policies, which improve air quality but reduce the cooling impact due the aerosol indirect effect on clouds\n(Fuglestvedt et al., 2009). Furthermore, these policies are implemented at different scales, with land transport being mostly\ntargeted at the national or regional (e.g. European) level, while shipping and aviation are regulated at the international level, may therefore affect both air quality and climate, with beneficial effects for both in some cases (e.g. when targeting BC, which\n45\nhas a warming effect on climate and a detrimental impact on air quality; Bond et al., 2013), and trade-offs in other cases, like\nsulphur reduction policies, which improve air quality but reduce the cooling impact due the aerosol indirect effect on clouds\n(Fuglestvedt et al., 2009). Furthermore, these policies are implemented at different scales, with land transport being mostly\ntargeted at the national or regional (e.g. 1\nIntroduction With a share of 14% (8.5 Pg) of the total CO2-equivalent emissions in 2018, the transport sectors are major contributors to\n25\nclimate change (Lamb et al., 2021). This share is dominated by land transport (including road and rail transport, and inland\nnavigation), which is responsible for 80.4% of it, while international shipping and aviation (international and domestic) account\nfor the remaining 8.4% and 11.2%, respectively. Moreover, the transport sectors are characterized by an approximately steady\ngrowth in emissions of greenhouse gases, at a rate of about 2% per year in the last three decades, mostly driven by developed countries (North America and Europe) and, since the beginning of the century, by the fast growing economies of East Asia\n30\n(Lamb et al., 2021). Traffic volumes are projected to grow further in the coming decades (Girod et al., 2013; Sims et al., 2014),\nmodulated by the current efforts to decarbonise this sector in order to achieve the goals of the Paris Agreement (Pietzcker et al.,\n2014; Creutzig et al., 2015; Esmeijer et al., 2020) and by the uncertain recovery path from the COVID-19 pandemic, which\nsignificantly affected the recent global emissions (Liu et al., 2020; Le Quéré et al., 2020). countries (North America and Europe) and, since the beginning of the century, by the fast growing economies of East Asia\n30\n(Lamb et al., 2021). Traffic volumes are projected to grow further in the coming decades (Girod et al., 2013; Sims et al., 2014),\nmodulated by the current efforts to decarbonise this sector in order to achieve the goals of the Paris Agreement (Pietzcker et al.,\n2014; Creutzig et al., 2015; Esmeijer et al., 2020) and by the uncertain recovery path from the COVID-19 pandemic, which\nsignificantly affected the recent global emissions (Liu et al., 2020; Le Quéré et al., 2020). Although the climate policies mostly target the emissions of CO2 and other greenhouse gases, the combustion processes\n35\npowering vehicles, ships and aircraft are also responsible for the emissions of several types of short-lived pollutants, such as\nnitrogen oxides (NOx=NO+NO2), carbon monoxide (CO), non-methane hydrocarbons (NMHC), sulphur dioxide (SO2) and\naerosol particles in form of black carbon (BC), organic carbon (OC) and primary aerosol sulfate (SO4). 1\nIntroduction European) level, while shipping and aviation are regulated at the international level, via the International Maritime Organization (IMO) and the International Civil Aviation Organization (ICAO), respectively, al-\n50\nthough regional policies are also applied in shipping, for instance the implementation of the Sulphur Emissions Control Areas\n(SECAs; Buhaug et al., 2009; Smith et al., 2014). To fully characterize the impact of short-lived pollutants from the transport\nsectors it is therefore necessary to consistently account for changes in the atmospheric composition and climate impact and, at\nthe same time, to address regional dependencies. In this study, we focus on the impact of transport-induced aerosol and aerosol precursor species. We update the assessments\n55\nof Righi et al. (2013, 2015, 2016) and improve them on three major aspects: In this study, we focus on the impact of transport-induced aerosol and aerosol precursor species. We update the assessments\n55\nof Righi et al. (2013, 2015, 2016) and improve them on three major aspects: In this study, we focus on the impact of transport-induced aerosol and aerosol precursor species. We update the assessments\n55\nof Righi et al. (2013, 2015, 2016) and improve them on three major aspects: 2 2 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 1. We use the recent CMIP6 emission inventory (Hoesly et al., 2018; van Marle et al., 2017), thus replacing the CMIP5\ninventory (Lamarque et al., 2010) applied in our previous studies and now consider the year 2015 instead of 2000 as\na reference for present-day conditions. 1. We use the recent CMIP6 emission inventory (Hoesly et al., 2018; van Marle et al., 2017), thus replacing the CMIP5\ninventory (Lamarque et al., 2010) applied in our previous studies and now consider the year 2015 instead of 2000 as\na reference for present-day conditions. 1. We use the recent CMIP6 emission inventory (Hoesly et al., 2018; van Marle et al., 2017), thus replacing the CMIP5\ninventory (Lamarque et al., 2010) applied in our previous studies and now consider the year 2015 instead of 2000 as\na reference for present-day conditions. 2. We implement the Shared Socioeconomic Pathways (SSPs; O’Neill et al., 2017) for the future projections, which are\na new generation of scenarios developed in support of the IPCC Sixth Assessment Report (Gidden et al., 2019). They\nare socio-economic scenarios that include assumptions on several indicators (e.g. 1\nIntroduction population, wealth, urbanisation, etc.)\nto describe the ability of the society to cope with climate change and are further combined with the climate pathways\nbased on the narratives of Representative Concentration Pathways (RCPs; van Vuuren et al., 2011) for climate policies,\nresulting in a matrix of possible futures. 60 65 3. We apply a newer version of the global chemistry-climate model EMAC (ECHAM-MESSy Atmospheric Chemistry;\nJöckel et al., 2010), featuring an updated aerosol scheme (MADE3, i.e. 3rd version of the Modal Aerosol Dynamics for\nEurope adapted for global applications; Kaiser et al., 2014) and a state-of-the-art two-moment cloud scheme (Kuebbeler\net al., 2014) accounting for aerosol-cloud interactions for all cloud phases (liquid, mixed-phase and ice). This model\nconfiguration has been thoroughly tuned and evaluated by Kaiser et al. (2019) and Righi et al. (2020), demonstrating its\nability to represent key aspects of aerosol, cloud and radiation processes. 70 Being key targets of decarbonisation and air pollution control policies, the transport sectors are subject of active research. Lund et al. (2014a) applied a global chemistry-transport model to quantify the effect of short-lived pollutants on climate when\nreplacing conventional diesel fuels with biofuels in the EU road transport sector and found a 15% and 80% reduction of Being key targets of decarbonisation and air pollution control policies, the transport sectors are subject of active research. Lund et al. (2014a) applied a global chemistry-transport model to quantify the effect of short-lived pollutants on climate when\nreplacing conventional diesel fuels with biofuels in the EU road transport sector and found a 15% and 80% reduction of radiative forcing (RF) when replacing 20% and 100% of diesel with biofuels, respectively. Lund et al. (2014b) considered\n75\nthe climate impact of black carbon from road diesel engines in 2010 and 2050 following a current-legislation scenario and\nfound that Europe gives the largest contribution in 2010, while South and East Asia dominate in 2050, as a result of different\nimplementation of reduction measures. The potential global relevance of local policies on land-based transport was highlighted\nby Hendricks et al. (2018), who estimated the climate impact of German land-based transport using a combination of modelling techniques and found that this is dominated by CO2, while the effect of short-lived pollutants is negligible. 1\nIntroduction This paper is organised as follows: Section 2 briefly describes the EMAC model with the aerosol submodel MADE3 and\nthe method used to estimate the impact of the transport sectors. A detailed analysis of the emission inventories for present-day climate is projected to remain relevant throughout the century, with strong regional variations. Hence, re-assessing the impact\n115\nof transport on aerosol concentrations, also with a focus on regional effects, is essential to understand the scope of the air\npollution measures covered by the SSPs narratives and the resulting climate impacts. This paper is organised as follows: Section 2 briefly describes the EMAC model with the aerosol submodel MADE3 and\nthe method used to estimate the impact of the transport sectors. A detailed analysis of the emission inventories for present-day climate is projected to remain relevant throughout the century, with strong regional variations. Hence, re-assessing the impact\n115\nof transport on aerosol concentrations, also with a focus on regional effects, is essential to understand the scope of the air\npollution measures covered by the SSPs narratives and the resulting climate impacts. This paper is organised as follows: Section 2 briefly describes the EMAC model with the aerosol submodel MADE3 and\nthe method used to estimate the impact of the transport sectors. A detailed analysis of the emission inventories for present-day This paper is organised as follows: Section 2 briefly describes the EMAC model with the aerosol submodel MADE3 and\nthe method used to estimate the impact of the transport sectors. A detailed analysis of the emission inventories for present-day\nand the future projections is provided in Sect. 3, focusing on the share of the three transport sectors on the total emissions of\n120\naerosol and precursors. The impact of transport on the aerosol concentration is discussed in Sect. 4, while Section 5 deals with\nthe climate impacts. A summary of the conclusions of this study is provided in Sect. 6. the method used to estimate the impact of the transport sectors. A detailed analysis of the emission inventories for present-day\nand the future projections is provided in Sect. 3, focusing on the share of the three transport sectors on the total emissions of\n120\naerosol and precursors. The impact of transport on the aerosol concentration is discussed in Sect. 4, while Section 5 deals with\nthe climate impacts. A summary of the conclusions of this study is provided in Sect. 6. 1\nIntroduction This effect was also simulated by Gettelman and Chen (2013), who however considered generally\n100\nsmaller particle sizes for aerosol sulfate in their assumptions, thus finding a larger RF in the range −164 to −23 mW m−2. A later study by Kapadia et al. (2016) supported these estimates and again found a reduction of the indirect effect under\na scenario with low aviation FSC, while Matthes et al. (2021) showed that the impact of aviation aerosol on low clouds may\nbe dependent on the cruise altitude of the global fleet. Aviation emissions of soot are also potentially impacting ice clouds (Hendricks et al., 2005, 2011; Penner et al., 2018), but this effect is very uncertain and strongly dependent on the assumed\n105\nice nucleating abilities of soot. In the model configuration adopted in the present study, we conservatively assume a very low\nice nucleating efficiency for aviation soot, so that its impact on climate via the interaction with natural cirrus clouds may be\nconsidered negligible (Righi et al., 2021). Despite active research on the impact of the transport sectors on atmosphere and climate, the literature on the impact of (Hendricks et al., 2005, 2011; Penner et al., 2018), but this effect is very uncertain and strongly dependent on the assumed\n105\nice nucleating abilities of soot. In the model configuration adopted in the present study, we conservatively assume a very low\nice nucleating efficiency for aviation soot, so that its impact on climate via the interaction with natural cirrus clouds may be\nconsidered negligible (Righi et al., 2021). Despite active research on the impact of the transport sectors on atmosphere and climate the literature on the impact of (Hendricks et al., 2005, 2011; Penner et al., 2018), but this effect is very uncertain and strongly dependent on the assumed\n105\nice nucleating abilities of soot. In the model configuration adopted in the present study, we conservatively assume a very low\nice nucleating efficiency for aviation soot, so that its impact on climate via the interaction with natural cirrus clouds may be\nconsidered negligible (Righi et al., 2021). 1\nIntroduction 95\nAs shown in Righi et al. (2013, hereafter R13), FSC is also critical for the aviation sector, as sulfate particles produced in\nthe aircraft exhaust plume could be transported downwards and potentially impact low level clouds, resulting in a significant\n(albeit quite uncertain) aerosol indirect effect. R13 estimated this effect to be between −70 and −15 mW m−2, depending on\nthe assumptions on the size of the emitted sulphate particles, but found no statistically significant effect when a low FSC was 2018) and its impacts on the regional and global climate. 95\nAs shown in Righi et al. (2013, hereafter R13), FSC is also critical for the aviation sector, as sulfate particles produced in\nthe aircraft exhaust plume could be transported downwards and potentially impact low level clouds, resulting in a significant\n(albeit quite uncertain) aerosol indirect effect. R13 estimated this effect to be between −70 and −15 mW m−2, depending on\nthe assumptions on the size of the emitted sulphate particles, but found no statistically significant effect when a low FSC was assumed for the aircraft fleet. This effect was also simulated by Gettelman and Chen (2013), who however considered generally\n100\nsmaller particle sizes for aerosol sulfate in their assumptions, thus finding a larger RF in the range −164 to −23 mW m−2. A later study by Kapadia et al. (2016) supported these estimates and again found a reduction of the indirect effect under\na scenario with low aviation FSC, while Matthes et al. (2021) showed that the impact of aviation aerosol on low clouds may\nbe dependent on the cruise altitude of the global fleet. Aviation emissions of soot are also potentially impacting ice clouds assumed for the aircraft fleet. This effect was also simulated by Gettelman and Chen (2013), who however considered generally\n100\nsmaller particle sizes for aerosol sulfate in their assumptions, thus finding a larger RF in the range −164 to −23 mW m−2. A later study by Kapadia et al. (2016) supported these estimates and again found a reduction of the indirect effect under\na scenario with low aviation FSC, while Matthes et al. (2021) showed that the impact of aviation aerosol on low clouds may\nbe dependent on the cruise altitude of the global fleet. Aviation emissions of soot are also potentially impacting ice clouds assumed for the aircraft fleet. 1\nIntroduction They also note,\n80\nhowever, that this might be different in other regions or countries, depending on the specific composition of the fleet and on\nits age, and also on the photochemical activity which varies considerably around the globe. The climate impact of ozone from\nglobal land-transport and shipping was quantified by Mertens et al. (2018), who applied an innovative tagging method to track\nthe emissions from specific sectors in a global model simulation and thus isolate the climate impact with better accuracy than in standard perturbation approaches. This resulted in higher land-transport- and shipping-induced concentrations of surface-level\n85\nozone and in a RF of 92 and 62 mW m−2, for the two sectors respectively. The shipping sector is particularly interesting due to\nthe new regulations introduced by MARPOL 73/78 (the International Convention for the Prevention of Pollution from Ships) in\nits Annex VI to globally limit fuel sulphur content (FSC) in shipping (Buhaug et al., 2009) and which took full effect in 2020. Sofiev et al. (2018), Kontovas (2020) and Bilsback et al. (2020) showed how reducing FSC leads to reduced SO2 emissions and lower shipping-induced aerosol concentrations: while this is beneficial for air quality and reduces premature mortality, it may\n90 3 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. also remove the cooling from the aerosol effects, hence accelerating global warming. Measurements of ship engine exhaust,\nhowever, show that particle number emission factors in low-sulphur fuels are not significantly decreased compared to standard\nheavy fuel oil (Kuittinen et al., 2021), implying that shipping would remain an important source of particle number even after\nthe MARPOL regulations. Another policy-relevant issue is the opening of Arctic routes in the near future (Stephenson et al.,\n2018) and its impacts on the regional and global climate. 95 also remove the cooling from the aerosol effects, hence accelerating global warming. Measurements of ship engine exhaust,\nhowever, show that particle number emission factors in low-sulphur fuels are not significantly decreased compared to standard\nheavy fuel oil (Kuittinen et al., 2021), implying that shipping would remain an important source of particle number even after\nthe MARPOL regulations. Another policy-relevant issue is the opening of Arctic routes in the near future (Stephenson et al.,\n2018) and its impacts on the regional and global climate. 95 2018) and its impacts on the regional and global climate. 1\nIntroduction Despite active research on the impact of the transport sectors on atmosphere and climate, the literature on the impact of Despite active research on the impact of the transport sectors on atmosphere and climate, the literature on the impact of\nspecific sectors in the context of the SSPs is still limited and a detailed analysis on the role of transport-induced aerosol is still\n110\nlacking. Lund et al. (2019) assessed the anthropogenic aerosol forcing in a subset of the SSP scenarios until the end of the\ncentury, concluding that the air pollution control measures assumed in these scenarios span a much broader range of emissions\nthan in the previous RCPs. Lund et al. (2020) used a climate response emulator to calculate the temperature response of various\nshort-lived compounds under the SSPs and compared it to the one of CO2, showing that the role of short-lived compounds on specific sectors in the context of the SSPs is still limited and a detailed analysis on the role of transport-induced aerosol is still\n110\nlacking. Lund et al. (2019) assessed the anthropogenic aerosol forcing in a subset of the SSP scenarios until the end of the\ncentury, concluding that the air pollution control measures assumed in these scenarios span a much broader range of emissions\nthan in the previous RCPs. Lund et al. (2020) used a climate response emulator to calculate the temperature response of various\nshort-lived compounds under the SSPs and compared it to the one of CO2, showing that the role of short-lived compounds on climate is projected to remain relevant throughout the century, with strong regional variations. Hence, re-assessing the impact\n115\nof transport on aerosol concentrations, also with a focus on regional effects, is essential to understand the scope of the air\npollution measures covered by the SSPs narratives and the resulting climate impacts. Thi\ni\ni\nd\nf ll\nS\nti\n2 b i fl\nd\nib\nth EMAC\nd l\nith th\nl\nb\nd l MADE3\nd climate is projected to remain relevant throughout the century, with strong regional variations. Hence, re-assessing the impact\n115\nof transport on aerosol concentrations, also with a focus on regional effects, is essential to understand the scope of the air\npollution measures covered by the SSPs narratives and the resulting climate impacts. 1\nIntroduction and the future projections is provided in Sect. 3, focusing on the share of the three transport sectors on the total emissions of\n120\naerosol and precursors. The impact of transport on the aerosol concentration is discussed in Sect. 4, while Section 5 deals with\nthe climate impacts. A summary of the conclusions of this study is provided in Sect. 6. 4 4 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 2\nModel and model simulations In this assessment we apply the global model EMAC with the aerosol submodel MADE3. EMAC is a chemistry and cli-\nmate model featuring several submodels to simulate tropospheric and middle atmospheric processes and their interaction with\n125\noceans, land, and human influences (Jöckel et al., 2010). EMAC is based on the second version of the Modular Earth Submodel\nSystem (MESSy) to link multi-institutional computer codes. The core atmospheric model is the ECHAM5 (fifth generation Eu-\nropean Centre Hamburg) general circulation model (Roeckner et al., 2006). Aerosols microphysics is represented by the aerosol\nsubmodel MADE3 (Kaiser et al., 2014), which considers aerosol sulfate, ammonium, nitrate, sea salt, particulate organic mat- ter, black carbon, mineral dust, and aerosol water. These species are distributed in nine aerosol modes, resulting from the\n130\ncombination of three size classes (Aitken, accumulation, and coarse) with three particle mixing states (soluble, insoluble and\nmixed particles). The representation of aerosol mass and number concentrations in MADE3 has been evaluated in detail in\nKaiser et al. (2019), using observational data from various station networks around the globe, from aircraft campaigns, and\nfrom satellite observations. ter, black carbon, mineral dust, and aerosol water. These species are distributed in nine aerosol modes, resulting from the\n130\ncombination of three size classes (Aitken, accumulation, and coarse) with three particle mixing states (soluble, insoluble and\nmixed particles). The representation of aerosol mass and number concentrations in MADE3 has been evaluated in detail in\nKaiser et al. (2019), using observational data from various station networks around the globe, from aircraft campaigns, and\nfrom satellite observations. For the present study, we apply the model configuration developed by Righi et al. (2020, 2021), which further includes an\n135\nexplicit representation of aerosol-radiation and aerosol-cloud interactions. Cloud microphysics and precipitation are dealt with\nby the CLOUD submodel, which in this configuration considers aerosol effects on warm, mixed-phase and cirrus clouds based\non the two-moment cloud scheme by Kuebbeler et al. (2014). Cloud-radiation and aerosol-radiation interactions are simulated\nby the submodels CLOUDOPT and RAD (Dietmüller et al., 2016), respectively. As the focus of this study lies on the radiative For the present study, we apply the model configuration developed by Righi et al. (2020, 2021), which further includes an\n135\nexplicit representation of aerosol-radiation and aerosol-cloud interactions. 2\nModel and model simulations Cloud microphysics and precipitation are dealt with\nby the CLOUD submodel, which in this configuration considers aerosol effects on warm, mixed-phase and cirrus clouds based\non the two-moment cloud scheme by Kuebbeler et al. (2014). Cloud-radiation and aerosol-radiation interactions are simulated\nby the submodels CLOUDOPT and RAD (Dietmüller et al., 2016), respectively. As the focus of this study lies on the radiative effects of aerosol and clouds, the concentrations of the radiatively active gases CO2, CH4, N2O, O3 and chlorofluorocarbons\n140\nare prescribed using globally averaged values for the year 2015. The studies by Kaiser et al. (2019) and Righi et al. (2020)\ndemonstrated the model’s ability to reproduce the relevant observed aerosol properties both close to the surface and in the\nmiddle and upper troposphere, as well as the main cloud and radiative properties. This configuration of EMAC is therefore\nsuited to the scope of the present study. effects of aerosol and clouds, the concentrations of the radiatively active gases CO2, CH4, N2O, O3 and chlorofluorocarbons\n140\nare prescribed using globally averaged values for the year 2015. The studies by Kaiser et al. (2019) and Righi et al. (2020)\ndemonstrated the model’s ability to reproduce the relevant observed aerosol properties both close to the surface and in the\nmiddle and upper troposphere, as well as the main cloud and radiative properties. This configuration of EMAC is therefore\nsuited to the scope of the present study. The chosen model resolution is T42L41, corresponding to a horizontal resolution of about 2.8\n◦× 2.8\n◦in latitude and longi-\n145\ntude (roughly 300 km at the Equator), with 41 non-equidistant vertical levels from the ground to about 5 hPa, mostly centered\nwithin the troposphere. All simulations cover a period of 11 years, between 2005 and 2015, with the first year used as spin-up\nand hence not included in the analysis. The model meteorology (temperature, winds and logarithm of the surface pressure) is\nnudged using the ERA-Interim reanalysis data for the same time period (Dee et al., 2011). The chosen model resolution is T42L41, corresponding to a horizontal resolution of about 2.8\n◦× 2.8\n◦in latitude and longi-\n145\ntude (roughly 300 km at the Equator), with 41 non-equidistant vertical levels from the ground to about 5 hPa, mostly centered\nwithin the troposphere. 2\nModel and model simulations We consider the results to be statistically significant when their As shown by R13, the perturbation method outlined above could lead to some inaccuracies in the estimated effects, due\nto the non-linearities in the involved processes, which affect in particular the secondary aerosol species. Alternative methods,\nsuch as for example tagging (Grewe, 2013; Rieger et al., 2018; Mertens et al., 2018), are hard to apply to the aerosol phase,\nespecially for secondary species, due to the difficulties in tracking the chemical processes taking place both in the gas phase and in the liquid phase (e.g., aerosol sulfate formation). Furthermore, the bulk of the climate effects estimated here are expected\n165\nto result from aerosol interactions with clouds, which cannot be isolated with the tagging approach, due to the prominent role\nof feedbacks. and in the liquid phase (e.g., aerosol sulfate formation). Furthermore, the bulk of the climate effects estimated here are expected\n165\nto result from aerosol interactions with clouds, which cannot be isolated with the tagging approach, due to the prominent role\nof feedbacks. 2\nModel and model simulations All simulations cover a period of 11 years, between 2005 and 2015, with the first year used as spin-up\nand hence not included in the analysis. The model meteorology (temperature, winds and logarithm of the surface pressure) is\nnudged using the ERA-Interim reanalysis data for the same time period (Dee et al., 2011). 150 As in R13, the effects of the transport sectors are quantified using the perturbation approach, in which a reference simulation\n150\nis compared to a sensitivity one where the emissions of a given sector are completely neglected. In the following sections, we\nshow both the absolute change (A) due to the emissions from the given sector and their relative contribution (R) to a specific\nquantity (e.g., burden or concentration). Considering, for instance, the land transport sector, these are defined as: As in R13, the effects of the transport sectors are quantified using the perturbation approach, in which a reference simulation\n150\nis compared to a sensitivity one where the emissions of a given sector are completely neglected. In the following sections, we\nshow both the absolute change (A) due to the emissions from the given sector and their relative contribution (R) to a specific\nquantity (e.g., burden or concentration). Considering, for instance, the land transport sector, these are defined as: A = REF −NOLAND\n(1) A = REF −NOLAND\n(1) (1) A = REF −NOLAND 5 5 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 155 (2) R = (REF −NOLAND)/REF where REF is the reference simulation and NOLAND the sensitivity simulation with land transport emissions switched off. The\nsame applies to the other two sectors, i.e. shipping and aviation. The statistical significance of A and R is verified by means\nof a two-tailed t-test with respect to the interannual variability. We consider the results to be statistically significant when their\nconfidence level is above 95%. 160 where REF is the reference simulation and NOLAND the sensitivity simulation with land transport emissions switched off. The\nsame applies to the other two sectors, i.e. shipping and aviation. The statistical significance of A and R is verified by means\nof a two-tailed t-test with respect to the interannual variability. 3\nEmissions of aerosol and precursors from the transport sectors The model simulations performed in this study are driven by the anthropogenic and open burni The model simulations performed in this study are driven by the anthropogenic and open burning emission datasets developed\nin support of the CMIP6 Project (Hoesly et al., 2018; van Marle et al., 2017). The emission data cover the historical period from\n170\nthe pre-industrial time (1750) to present-day (2014), and future projections until the year 2100. Here, we consider a present\nday case (2015) and two projections, for a near- (2030) and mid-term (2050) future, both under three SSPs: SSP1 (van Vuuren\net al., 2017), SSP2 (Fricko et al., 2017), and SSP3 (Fujimori et al., 2017). Although more SSPs are available in this inventory,\nthe three SSPs analysed here are fairly representative of possible futures, including: a sustainability scenario (SSP1) with in support of the CMIP6 Project (Hoesly et al., 2018; van Marle et al., 2017). The emission data cover the historical period from\n170\nthe pre-industrial time (1750) to present-day (2014), and future projections until the year 2100. Here, we consider a present\nday case (2015) and two projections, for a near- (2030) and mid-term (2050) future, both under three SSPs: SSP1 (van Vuuren\net al., 2017), SSP2 (Fricko et al., 2017), and SSP3 (Fujimori et al., 2017). Although more SSPs are available in this inventory,\nthe three SSPs analysed here are fairly representative of possible futures, including: a sustainability scenario (SSP1) with strong technology development and environmental concerns; a middle-of-the-road scenario (SSP2) reflecting a continuation\n175\nof historical trends in many aspects; and a regionally heterogeneous scenario (SSP3) characterized by slow economic growth,\nweak technology development and, most importantly for the present assessment, by a high level of air pollutant emissions,\nincluding aerosol. We do not consider the long-term future after 2050, as the projections beyond this time horizon are affected\nby large uncertainties in their driving forces (such as demographic changes, economic growth and technological development; see Bauer et al., 2017). The reason for choosing 2015 (from the SSP2 scenario) as the reference for the present day, instead of\n180\nthe last year of the historical CMIP6 time-series (2014), is to have consistent emissions from the same database, i.e. the SSPs. Note, however, that the small differences between 2014 and 2015 are negligible for the scope of the present assessment. 3\nEmissions of aerosol and precursors from the transport sectors A series of specific assumptions corresponds to each of the SSP narratives, concerning, for instance, population and eco-\nnomic growth, urbanisation level, policy implementation, technological advancements, availability of resources, human and societal developments, etc. These assumptions are then elaborated by integrated assessment models to produce emission pro-\n185\njections for greenhouse gases, short-lived gases and aerosol particles. Note that in their reference version, the SSPs storylines societal developments, etc. These assumptions are then elaborated by integrated assessment models to produce emission pro-\n185\njections for greenhouse gases, short-lived gases and aerosol particles. Note that in their reference version, the SSPs storylines 6 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. do not assume any implementation of climate policies. In the matrix framework of the SSPs (van Vuuren et al., 2014), climate\npolicies are included at a later stage, when the reference scenarios are combined with mitigation scenarios using the same\napproach as in the RCPs (Moss et al., 2010; van Vuuren et al., 2011), i.e., targeting a given anthropogenic RF in the year 2100. do not assume any implementation of climate policies. In the matrix framework of the SSPs (van Vuuren et al., 2014), climate\npolicies are included at a later stage, when the reference scenarios are combined with mitigation scenarios using the same\napproach as in the RCPs (Moss et al., 2010; van Vuuren et al., 2011), i.e., targeting a given anthropogenic RF in the year 2100. Among the various combinations of the SSPs with forcing pathways (Gidden et al., 2019), we choose here SSP1-1.9 (targeting\n190\nan anthropogenic RF of 1.9 W m−2 in 2100 and matching the 1.5◦C goal of the Paris Agreement), SSP2-4.5 (RF of 4.5 W m−2\nin 2100, close to the previous RCP4.5 scenario), and SSP3-7.0 (RF of 7.0 W m−2 in 2100). For simplicity, in the following we\nwill refer to the SSPs just by their number and skip the RF indication, as there is no ambiguity. do not assume any implementation of climate policies. In the matrix framework of the SSPs (van Vuuren et al., 2014), climate\npolicies are included at a later stage, when the reference scenarios are combined with mitigation scenarios using the same\napproach as in the RCPs (Moss et al., 2010; van Vuuren et al., 2011), i.e., targeting a given anthropogenic RF in the year 2100. 3\nEmissions of aerosol and precursors from the transport sectors Shipping and aviation contributions are generally small, but one should note that ships and\n200\naircraft mostly travel in regions which are otherwise hardly affected by anthropogenic emissions, hence their local contribution\nto atmospheric aerosol concentrations can be relevant even though the emissions are low. Future emissions in 2030 and 2050 strongly decrease with respect to present day in SSP1, consistently with the narrative\nunderlying this scenario. According to van Vuuren et al. (2017), SSP1 is characterized by a rapid decline in costs for clean especially for land transport). Shipping and aviation contributions are generally small, but one should note that ships and\n200\naircraft mostly travel in regions which are otherwise hardly affected by anthropogenic emissions, hence their local contribution\nto atmospheric aerosol concentrations can be relevant even though the emissions are low. Future emissions in 2030 and 2050 strongly decrease with respect to present day in SSP1, consistently with the narrative\nunderlying this scenario. According to van Vuuren et al. (2017), SSP1 is characterized by a rapid decline in costs for clean technologies, favoring a rapid transition to electric vehicles, and by an increase in public transport and car sharing. It further\n205\nassumes the worldwide implementation of strict air-quality policies, currently only applied in OECD countries. The high share\nof electric vehicles (up to 75%) leads to a strong decrease in NOx emissions in SSP1, whose contribution to the total is reduced\nfrom 26% in 2015 to 16% in 2030 and further down to 6.7% in 2050, while the contribution of aviation remains constant around\n2% and the shipping one is only reduced in 2050, due to the known challenges in electrifying these sectors. A similar trend can technologies, favoring a rapid transition to electric vehicles, and by an increase in public transport and car sharing. It further\n205\nassumes the worldwide implementation of strict air-quality policies, currently only applied in OECD countries. The high share\nof electric vehicles (up to 75%) leads to a strong decrease in NOx emissions in SSP1, whose contribution to the total is reduced\nfrom 26% in 2015 to 16% in 2030 and further down to 6.7% in 2050, while the contribution of aviation remains constant around\n2% and the shipping one is only reduced in 2050, due to the known challenges in electrifying these sectors. A similar trend can be seen for the other species. 3\nEmissions of aerosol and precursors from the transport sectors The reduction in SO2 emissions is stronger than for other species in SSP1, mostly driven by the\n210\ndecrease in the non-traffic anthropogenic emissions, which, however, maintain a roughly constant share around 85–90% of the\ntotal. The shipping sector contribution to SO2 remains relatively high until 2030, decreasing only slightly from 9% in 2015 to\n7% in 2030. This could be due to the phase-out of traditional (low-sulphur) biofuels in 2030 in this scenario and to the lack of\nalternative technologies before 2050, when the share is strongly reduced to 1.7%. be seen for the other species. The reduction in SO2 emissions is stronger than for other species in SSP1, mostly driven by the\n210\ndecrease in the non-traffic anthropogenic emissions, which, however, maintain a roughly constant share around 85–90% of the\ntotal. The shipping sector contribution to SO2 remains relatively high until 2030, decreasing only slightly from 9% in 2015 to\n7% in 2030. This could be due to the phase-out of traditional (low-sulphur) biofuels in 2030 in this scenario and to the lack of\nalternative technologies before 2050, when the share is strongly reduced to 1.7%. be seen for the other species. The reduction in SO2 emissions is stronger than for other species in SSP1, mostly driven by the\n210\ndecrease in the non-traffic anthropogenic emissions, which, however, maintain a roughly constant share around 85–90% of the\ntotal. The shipping sector contribution to SO2 remains relatively high until 2030, decreasing only slightly from 9% in 2015 to\n7% in 2030. This could be due to the phase-out of traditional (low-sulphur) biofuels in 2030 in this scenario and to the lack of\nalternative technologies before 2050, when the share is strongly reduced to 1.7%. In contrast to the optimistic SSP1 scenario, the SSP2 narrative (Fricko et al., 2017) assumes a significant use of fossil\n215\nfuel in the future, combined with less stringent air pollution reduction measures. This leads to an only moderate reduction in\nthe emissions of short-lived compounds in the future. The share of NOx emissions by land-transport and shipping remains\napproximately constant until 2050 (around 25% and 14%, respectively), while for aviation it almost doubles in 2030 (from\n1.9% to 3.4%) and triples in 2050 (5.3%). 3\nEmissions of aerosol and precursors from the transport sectors Among the various combinations of the SSPs with forcing pathways (Gidden et al., 2019), we choose here SSP1-1.9 (targeting\n190\nan anthropogenic RF of 1.9 W m−2 in 2100 and matching the 1.5◦C goal of the Paris Agreement), SSP2-4.5 (RF of 4.5 W m−2 Among the various combinations of the SSPs with forcing pathways (Gidden et al., 2019), we choose here SSP1-1.9 (targeting\n190\nan anthropogenic RF of 1.9 W m−2 in 2100 and matching the 1.5◦C goal of the Paris Agreement), SSP2-4.5 (RF of 4.5 W m−2\nin 2100, close to the previous RCP4.5 scenario), and SSP3-7.0 (RF of 7.0 W m−2 in 2100). For simplicity, in the following we\nwill refer to the SSPs just by their number and skip the RF indication, as there is no ambiguity. The total emissions of aerosol and precursor gases from all sectors in the CMIP6 inventory are summarized in Fig 1 for Among the various combinations of the SSPs with forcing pathways (Gidden et al., 2019), we choose here SSP1-1.9 (targeting\n190\nan anthropogenic RF of 1.9 W m−2 in 2100 and matching the 1.5◦C goal of the Paris Agreement), SSP2-4.5 (RF of 4.5 W m−2\nin 2100, close to the previous RCP4.5 scenario), and SSP3-7.0 (RF of 7.0 W m−2 in 2100). For simplicity, in the following we\nwill refer to the SSPs just by their number and skip the RF indication, as there is no ambiguity. The total emissions of aerosol and precursor gases from all sectors in the CMIP6 inventory are summarized in Fig. 1, for The total emissions of aerosol and precursor gases from all sectors in the CMIP6 inventory are summarized in Fig. 1, for\nthe present-day (2015) and the three SSPs for the years 2030 and 2050. The total emissions of the anthropogenic non-transport\n195\nsectors (comprising energy, industry, residential and commercial, agriculture, solvents production and application, and waste)\ndominate the emission budget of all species in all scenarios, with relative contributions over 90% to NH3 emissions (Fig. 1c)\nmostly due to the agriculture sector. The transport sectors are particularly relevant for NOx emissions (Fig. 1a), with large\nrelative contributions from land transport and shipping (mainly driven by emissions from diesel engines) and BC (Fig. 1e, especially for land transport). 3\nEmissions of aerosol and precursors from the transport sectors This is related to the increase in air traffic volumes combined with the lack of In contrast to the optimistic SSP1 scenario, the SSP2 narrative (Fricko et al., 2017) assumes a significant use of fossil\n215\nfuel in the future, combined with less stringent air pollution reduction measures. This leads to an only moderate reduction in\nthe emissions of short-lived compounds in the future. The share of NOx emissions by land-transport and shipping remains\napproximately constant until 2050 (around 25% and 14%, respectively), while for aviation it almost doubles in 2030 (from\n1.9% to 3.4%) and triples in 2050 (5.3%). This is related to the increase in air traffic volumes combined with the lack of In contrast to the optimistic SSP1 scenario, the SSP2 narrative (Fricko et al., 2017) assumes a significant use of fossil\n215\nfuel in the future, combined with less stringent air pollution reduction measures. This leads to an only moderate reduction in\nthe emissions of short-lived compounds in the future. The share of NOx emissions by land-transport and shipping remains\napproximately constant until 2050 (around 25% and 14%, respectively), while for aviation it almost doubles in 2030 (from\n1.9% to 3.4%) and triples in 2050 (5.3%). This is related to the increase in air traffic volumes combined with the lack of efficient alternative technologies for emissions reduction in this sector, which only in SSP1 is counteracted by a reduction in\n220\nair traffic. Similar trends can be seen for the other species in Fig. 1, with the exception of SO2 for shipping. In this case, a very efficient alternative technologies for emissions reduction in this sector, which only in SSP1 is counteracted by a reduction in\n220\nair traffic. Similar trends can be seen for the other species in Fig. 1, with the exception of SO2 for shipping. In this case, a very 7 7 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 3\nEmissions of aerosol and precursors from the transport sectors 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n25\n50\n75\n100\n125\n150\n175\n[Tg(NO2) yr\n1]\n29\n14\n2.2\n40\n15\n26\n14\n1.9\n44\n13\n16\n15\n2.1\n47\n20\n23\n14\n3.4\n46\n13\n27\n11\n2.6\n48\n11\n6.7\n2.1\n2.1\n57\n33\n26\n14\n5.3\n40\n15\n29\n7.4\n3.3\n49\n11\n(a) - NOx emissions\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n200\n400\n600\n800\n1000\n[Tg(CO) yr\n1]\n21\n0.1\n<0.1\n34\n45\n14\n<0.1\n<0.1\n45\n41\n12\n0.1\n0.1\n36\n52\n15\n<0.1\n0.1\n48\n36\n15\n<0.1\n<0.1\n52\n33\n5.0\n<0.1\n0.1\n37\n58\n18\n<0.1\n0.1\n43\n39\n15\n<0.1\n0.1\n53\n32\n(b) - CO emissions\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n20\n40\n60\n80\n[Tg(NH3) yr\n1]\n1.0\n<0.1\n<0.1\n75\n24\n0.6\n<0.1\n<0.1\n92\n7.4\n0.6\n<0.1\n<0.1\n94\n4.9\n0.5\n<0.1\n<0.1\n94\n5.8\n0.7\n<0.1\n<0.1\n93\n6.0\n0.6\n<0.1\n<0.1\n95\n4.6\n0.4\n<0.1\n<0.1\n94\n6.0\n0.7\n<0.1\n<0.1\n93\n6.5\n(c) - NH3 emissions\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n20\n40\n60\n80\n100\n[Tg(SO2) yr\n1]\n4.0\n10\n0.2\n82\n3.8\n3.2\n9.1\n0.3\n85\n2.4\n1.6\n7.0\n0.8\n86\n5.1\n2.9\n3.1\n0.5\n91\n2.8\n4.4\n6.6\n0.4\n86\n2.2\n0.6\n1.7\n1.3\n88\n8.1\n3.7\n0.4\n0.9\n91\n4.1\n5.0\n3.4\n0.5\n89\n2.3\n(d) - SO2 emissions\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n2\n4\n6\n8\n10\n[Tg(BC) yr\n1]\n17\n1.7\n<0.1\n45\n35\n14\n1.7\n0.1\n65\n19\n12\n2.6\n0.2\n53\n33\n15\n1.9\n0.2\n62\n21\n14\n1.3\n0.1\n68\n16\n5.1\n0.9\n0.1\n44\n50\n19\n0.3\n0.3\n56\n24\n16\n0.9\n0.2\n67\n15\n(e) - BC emissions\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n10\n20\n30\n40\n[Tg(OC) yr\n1]\n4.0\n0.4\n<0.1\n29\n67\n1.6\n0.3\n<0.1\n49\n49\n0.6\n0.6\n<0.1\n40\n59\n1.2\n0.4\n<0.1\n53\n46\n1.7\n0.3\n<0.1\n58\n40\n0.2\n0.5\n<0.1\n34\n65\n1.3\n<0.1\n<0.1\n49\n50\n1.8\n0.3\n<0.1\n60\n38\n(f) - OC emissions\nLand transport\nShipping\nAviation\nOther anthrop. 3\nEmissions of aerosol and precursors from the transport sectors Open burning\nFigure 1. Total emissions of each species from the different sectors in the CMIP6/SSP inventory, compared with the year 2000 emissions\nfrom the CMIP5 inventory (Lamarque et al., 2010) used in Righi et al. (2013). The numbers on the left of each bar indicate the relative\ncontribution (in percent) of each sector to the total. OC emissions are multiplied by a factor 1.4 in the model to obtain particulate organic\nmatter (POM) emissions, as required by the MADE3 aerosol submodel. Furthermore, a small fraction (∼2%, depending on the sector) of the\nSO2 emitted mass is emitted as primary aerosol sulfate in the model (see Kaiser et al., 2019, for more details). Note that the vertical scales\ndiffer among the panels. 3\nEmissions of aerosol and precursors from the transport sectors 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n200\n400\n600\n800\n1000\n[Tg(CO) yr\n1]\n21\n0.1\n<0.1\n34\n45\n14\n<0.1\n<0.1\n45\n41\n12\n0.1\n0.1\n36\n52\n15\n<0.1\n0.1\n48\n36\n15\n<0.1\n<0.1\n52\n33\n5.0\n<0.1\n0.1\n37\n58\n18\n<0.1\n0.1\n43\n39\n15\n<0.1\n0.1\n53\n32\n(b) - CO emissions\n(d)\nSO\nemissions 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n25\n50\n75\n100\n125\n150\n175\n[Tg(NO2) yr\n1]\n29\n14\n2.2\n40\n15\n26\n14\n1.9\n44\n13\n16\n15\n2.1\n47\n20\n23\n14\n3.4\n46\n13\n27\n11\n2.6\n48\n11\n6.7\n2.1\n2.1\n57\n33\n26\n14\n5.3\n40\n15\n29\n7.4\n3.3\n49\n11\n(a) - NOx emissions\n(c)\nNH\nemissions 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n20\n40\n60\n80\n100\n[Tg(SO2) yr\n1]\n4.0\n10\n0.2\n82\n3.8\n3.2\n9.1\n0.3\n85\n2.4\n1.6\n7.0\n0.8\n86\n5.1\n2.9\n3.1\n0.5\n91\n2.8\n4.4\n6.6\n0.4\n86\n2.2\n0.6\n1.7\n1.3\n88\n8.1\n3.7\n0.4\n0.9\n91\n4.1\n5.0\n3.4\n0.5\n89\n2.3\n(d) - SO2 emissions 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n20\n40\n60\n80\n[Tg(NH3) yr\n1]\n1.0\n<0.1\n<0.1\n75\n24\n0.6\n<0.1\n<0.1\n92\n7.4\n0.6\n<0.1\n<0.1\n94\n4.9\n0.5\n<0.1\n<0.1\n94\n5.8\n0.7\n<0.1\n<0.1\n93\n6.0\n0.6\n<0.1\n<0.1\n95\n4.6\n0.4\n<0.1\n<0.1\n94\n6.0\n0.7\n<0.1\n<0.1\n93\n6.5\n(c) - NH3 emissions\n( )\nC\ni\ni 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n2\n4\n6\n8\n10\n[Tg(BC) yr\n1]\n17\n1.7\n<0.1\n45\n35\n14\n1.7\n0.1\n65\n19\n12\n2.6\n0.2\n53\n33\n15\n1.9\n0.2\n62\n21\n14\n1.3\n0.1\n68\n16\n5.1\n0.9\n0.1\n44\n50\n19\n0.3\n0.3\n56\n24\n16\n0.9\n0.2\n67\n15\n(e) - BC emissions\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n0\n10\n20\n30\n40\n[Tg(OC) yr\n1]\n4.0\n0.4\n<0.1\n29\n67\n1.6\n0.3\n<0.1\n49\n49\n0.6\n0.6\n<0.1\n40\n59\n1.2\n0.4\n<0.1\n53\n46\n1.7\n0.3\n<0.1\n58\n40\n0.2\n0.5\n<0.1\n34\n65\n1.3\n<0.1\n<0.1\n49\n50\n1.8\n0.3\n<0.1\n60\n38\n(f) - OC emissions\nLand transport\nShipping\nAviation\nOther anthrop. 3\nEmissions of aerosol and precursors from the transport sectors Open burning Figure 1. Total emissions of each species from the different sectors in the CMIP6/SSP inventory, compared with the year 2000 emissions\nfrom the CMIP5 inventory (Lamarque et al., 2010) used in Righi et al. (2013). The numbers on the left of each bar indicate the relative\ncontribution (in percent) of each sector to the total. OC emissions are multiplied by a factor 1.4 in the model to obtain particulate organic\nmatter (POM) emissions, as required by the MADE3 aerosol submodel. Furthermore, a small fraction (∼2%, depending on the sector) of the\nSO2 emitted mass is emitted as primary aerosol sulfate in the model (see Kaiser et al., 2019, for more details). Note that the vertical scales\ndiffer among the panels. 8 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. marked decrease in the share of this sector is evident in SSP2, decreasing from 9.1% in 2015 to 3.1% in 2030 and 0.4% in\n2050, representing the lowest share of this sector among the three investigated scenarios. SSP3 represents a more pessimistic scenario, implementing weak air pollution reduction measures, combined with continued\ndependency on fossil fuels and slow implementation of new technologies (Fujimori et al., 2017). This scenario further considers\n225\na late saturation in transport demand, combined with a low electrification level (up to 10%). This leads to an increase in the\nland-transport share of NOx which, together with the increase in the other non-traffic anthropogenic sources, drives the overall\nincrease of NOx emissions in SSP3 in 2030. The total NOx emissions remain then approximately constant until 2050, thanks\nto the reduced share of the shipping sector and the shift to alternative fuels. Due to the rapid shift to high speed transport modes\nassumed in SSP3 the share of aviation increases as well from 1 9% in 2015 to 2 6% in 2030 and 3 3% in 2050 although this\n230 SSP3 represents a more pessimistic scenario, implementing weak air pollution reduction measures, combined with continued\ndependency on fossil fuels and slow implementation of new technologies (Fujimori et al., 2017). This scenario further considers\n225\na late saturation in transport demand, combined with a low electrification level (up to 10%). This leads to an increase in the\nland-transport share of NOx which, together with the increase in the other non-traffic anthropogenic sources, drives the overall\nincrease of NOx emissions in SSP3 in 2030. 3\nEmissions of aerosol and precursors from the transport sectors The total NOx emissions remain then approximately constant until 2050, thanks\nto the reduced share of the shipping sector and the shift to alternative fuels. Due to the rapid shift to high speed transport modes assumed in SSP3, the share of aviation increases as well, from 1.9% in 2015 to 2.6% in 2030 and 3.3% in 2050, although this\n230\nincrease is less marked than in SSP2. An analogous trend is also evident for SO2, BC and OC emissions, although the latter is\npartly compensated by a reduction of open burning emissions in 2030 and 2050. Since in the present work we use an updated inventory (CMIP6/SSP) with respect to our previous assessment (CMIP5/RCP;\nR13), in Fig. 1 we also compare the total emissions between these two inventories. Note that not only the inventory was ),\ng\np\ny\ny\nupdated, but we also considered a different baseline year for present-day conditions (2015 instead of 2000). A general increase\n235\nin the total emissions can be seen, mostly driven by the anthropogenic non-transport sectors, while only the emissions of\nCO (Fig. 1b) and SO2 (Fig. 1d) slightly decrease. The transport sectors mostly show a decrease in emissions between 2000\nand 2015, both in absolute and relative terms, with the notable exception of NOx (Fig. 1a) and BC (Fig. 1e), whose share,\nhowever, decreased from 29% to 26% and from 17% to 14%, respectively. This indicates that, although the overall growth updated, but we also considered a different baseline year for present-day conditions (2015 instead of 2000). A general increase\n235\nin the total emissions can be seen, mostly driven by the anthropogenic non-transport sectors, while only the emissions of\nCO (Fig. 1b) and SO2 (Fig. 1d) slightly decrease. The transport sectors mostly show a decrease in emissions between 2000\nand 2015, both in absolute and relative terms, with the notable exception of NOx (Fig. 1a) and BC (Fig. 1e), whose share,\nhowever, decreased from 29% to 26% and from 17% to 14%, respectively. This indicates that, although the overall growth updated, but we also considered a different baseline year for present-day conditions (2015 instead of 2000). A general increase\n235\nin the total emissions can be seen, mostly driven by the anthropogenic non-transport sectors, while only the emissions of\nCO (Fig. 1b) and SO2 (Fig. 1d) slightly decrease. 3\nEmissions of aerosol and precursors from the transport sectors The transport sectors mostly show a decrease in emissions between 2000\nand 2015, both in absolute and relative terms, with the notable exception of NOx (Fig. 1a) and BC (Fig. 1e), whose share,\nhowever, decreased from 29% to 26% and from 17% to 14%, respectively. This indicates that, although the overall growth of land transport volumes leads of course to an increase in emissions of these two highly relevant species for this sector, the\n240\nintroduction of air pollution control measures (e.g., on diesel engines) in the developed countries helped to reduce the relative\nimpact of the sector on the global emission budget. Another interesting case is SO2 (Fig. 1d) from shipping, which shows\na decrease in both absolute and relative terms between 2000 and 2015. This is a consequence of the limits on shipping FSC\nintroduced starting from 2010 in the MARPOL Annex VI (Buhaug et al., 2009). These measures also affected shipping NOx of land transport volumes leads of course to an increase in emissions of these two highly relevant species for this sector, the\n240\nintroduction of air pollution control measures (e.g., on diesel engines) in the developed countries helped to reduce the relative\nimpact of the sector on the global emission budget. Another interesting case is SO2 (Fig. 1d) from shipping, which shows\na decrease in both absolute and relative terms between 2000 and 2015. This is a consequence of the limits on shipping FSC\nintroduced starting from 2010 in the MARPOL Annex VI (Buhaug et al., 2009). These measures also affected shipping NOx of land transport volumes leads of course to an increase in emissions of these two highly relevant species for this sector, the\n240\nintroduction of air pollution control measures (e.g., on diesel engines) in the developed countries helped to reduce the relative\nimpact of the sector on the global emission budget. Another interesting case is SO2 (Fig. 1d) from shipping, which shows\na decrease in both absolute and relative terms between 2000 and 2015. This is a consequence of the limits on shipping FSC\nintroduced starting from 2010 in the MARPOL Annex VI (Buhaug et al., 2009). These measures also affected shipping NOx emissions, which however still grew between 2000 and 2015 (Fig. 1a), but keeping a stable share of 14% of the total. 3\nEmissions of aerosol and precursors from the transport sectors The\n245\nemissions from the aviation sector increase for all species between 2000 and 2015, which is expected since emission reduction\nmeasures in this sector are over-compensated by the high growth rates in traffic volumes during this period. An important\nimprovement in the CMIP6 inventory is the consideration of the emissions from more species in the aviation sector, while in\nCMIP5 only NOx and BC were available. In R13 we nevertheless included SO2 emissions from aviation, deriving them from\nBC emissions by scaling with the respective emission factors of the two species\n250 emissions, which however still grew between 2000 and 2015 (Fig. 1a), but keeping a stable share of 14% of the total. The\n245\nemissions from the aviation sector increase for all species between 2000 and 2015, which is expected since emission reduction\nmeasures in this sector are over-compensated by the high growth rates in traffic volumes during this period. An important\nimprovement in the CMIP6 inventory is the consideration of the emissions from more species in the aviation sector, while in\nCMIP5 only NOx and BC were available. In R13 we nevertheless included SO2 emissions from aviation, deriving them from emissions, which however still grew between 2000 and 2015 (Fig. 1a), but keeping a stable share of 14% of the total. The\n245\nemissions from the aviation sector increase for all species between 2000 and 2015, which is expected since emission reduction\nmeasures in this sector are over-compensated by the high growth rates in traffic volumes during this period. An important\nimprovement in the CMIP6 inventory is the consideration of the emissions from more species in the aviation sector, while in\nCMIP5 only NOx and BC were available. In R13 we nevertheless included SO2 emissions from aviation, deriving them from BC emissions by scaling with the respective emission factors of the two species. 250\nThe emission inventories applied here only provide data for the emitted mass of each compounds, whereas the MADE3\naerosol submodel requires emission fluxes for both mass and number. The latter is derived by assuming typical log-normal size\ndistributions for the emitted particles in each sector, based on measurements when available. Further assumptions are made to\nassign the emitted mass to the various modes of MADE3. This follows the same approach of Kaiser et al. (2019), summarized BC emissions by scaling with the respective emission factors of the two species. 3\nEmissions of aerosol and precursors from the transport sectors 250\nThe emission inventories applied here only provide data for the emitted mass of each compounds, whereas the MADE3\naerosol submodel requires emission fluxes for both mass and number. The latter is derived by assuming typical log-normal size\ndistributions for the emitted particles in each sector, based on measurements when available. Further assumptions are made to\nassign the emitted mass to the various modes of MADE3. This follows the same approach of Kaiser et al. (2019), summarized BC emissions by scaling with the respective emission factors of the two species. 250\nThe emission inventories applied here only provide data for the emitted mass of each compounds, whereas the MADE3\naerosol submodel requires emission fluxes for both mass and number. The latter is derived by assuming typical log-normal size\ndistributions for the emitted particles in each sector, based on measurements when available. Further assumptions are made to\nassign the emitted mass to the various modes of MADE3. This follows the same approach of Kaiser et al. (2019), summarized in Table 1. For the open burning sector, we follow the AeroCom recommendations (Dentener et al., 2006) and assign the aerosol\n255\nemissions of primary sulfate (SO4), BC and OC to the accumulation mode. For the anthropogenic non-traffic sector, we use the in Table 1. For the open burning sector, we follow the AeroCom recommendations (Dentener et al., 2006) and assign the aerosol\n255\nemissions of primary sulfate (SO4), BC and OC to the accumulation mode. For the anthropogenic non-traffic sector, we use the in Table 1. For the open burning sector, we follow the AeroCom recommendations (Dentener et al., 2006) and assign the aerosol\n255\nemissions of primary sulfate (SO4), BC and OC to the accumulation mode. For the anthropogenic non-traffic sector, we use the 9 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Table 1. Summary of the emission setup used for the model simulations in this study, together with the log-normal size distribution param-\neters applied to derive number emission fluxes from the mass. MF is the mass fraction in the Aitken (akn) and accumulation (acc) mode. D (geometric mean diameter) and σ (standard deviation) are the parameters of the log-normal distribution. See Kaiser et al. (2019) and\nreferences therein for a detailed discussions about these parameters. 3\nEmissions of aerosol and precursors from the transport sectors Sector\nSpecie\nMFakn [%]\nDakn [nm]\nσakn\nMFacc [%]\nDacc [nm]\nσacc\nOpen burning\nSO4\n–\n–\n–\n100\n80\n1.8\nBC/POM\n–\n–\n–\n100\n80\n1.8\nAntrop. non-traffic\nSO4\n–\n–\n–\n–\n–\n–\nBC/POM\n–\n–\n–\n100\n138\n1.59\nLand transport\nSO4\n100\n30\n1.80\n–\n–\n–\nBC/POM\n10\n58\n1.58\n90\n138\n1.59\nShipping\nSO4\n10\n70\n1.45\n90\n260\n1.25\nBC/POM\n10\n70\n1.45\n90\n260\n1.25\nAviation\nSO4\n91\n25\n1.55\n9\n150\n1.65\nBC/POM\n91\n25\n1.55\n9\n150\n1.65 parameters measured by Birmili et al. (2009) in the urban background of Berlin, hence assuming an aged aerosol population. This assigns the emissions to the accumulation mode only. Note that no primary SO4 emissions are considered for this sector. We use the same parameters also for land transport, but in this case we assign 10% of the emitted mass to the Aitken mode, representative of a fresh aerosol population in the urban areas close to the emission sources. Primary SO4 emissions from land\n260\ntransport follow again the AeroCom recommendations, assigning the emitted mass to Aitken mode only (note, however, that\nthe land transport emissions of SO4 are relatively low due to the low-sulphur content of gasoline and diesel fuels used for land\ntransport vehicles in most regions of the world). For shipping, we used the parameters measured by Petzold et al. (2008) in ship\nplumes. As for the land transport sector, this assumes a 10/90% split of emitted mass between Aitken and accumulation mode, respectively, but with much larger particles in the accumulation mode (260 nm vs. 138 nm). As noted by Petzold et al. (2008)\n265\nand R13, due to a more efficient mixing, the aging process is particularly effective in the marine boundary layer, leading to\na relatively fast growth of particles in ship plumes. In-situ measurements in actual plumes are also used to derive the parameters\nfor the aviation emissions (Petzold et al., 1999). Due to the high burning efficiency of aircraft turbines, the emitted BC and\nOC particles are small, with an Aitken mode at 25 nm median diameter comprising about 90% of the emitted mass. Here we respectively, but with much larger particles in the accumulation mode (260 nm vs. 138 nm). As noted by Petzold et al. 3\nEmissions of aerosol and precursors from the transport sectors (2008)\n265\nand R13, due to a more efficient mixing, the aging process is particularly effective in the marine boundary layer, leading to\na relatively fast growth of particles in ship plumes. In-situ measurements in actual plumes are also used to derive the parameters\nfor the aviation emissions (Petzold et al., 1999). Due to the high burning efficiency of aircraft turbines, the emitted BC and\nOC particles are small, with an Aitken mode at 25 nm median diameter comprising about 90% of the emitted mass. Here we conservatively assume the same size distribution also for primary SO4, although their actual size is much more uncertain and\n270\nsome studies (e.g. Kärcher et al., 2007) show that small, nucleation-size sulfate particles of the order of a few nanometers are\npresent in aircraft plumes during the dispersion phase. conservatively assume the same size distribution also for primary SO4, although their actual size is much more uncertain and\n270\nsome studies (e.g. Kärcher et al., 2007) show that small, nucleation-size sulfate particles of the order of a few nanometers are\npresent in aircraft plumes during the dispersion phase. 10 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4\nTransport impact on aerosol concentrations Fig. 2 shows the impact of land transport, shipping and aviation on the burdens of various aerosol species and of aerosol\nparticle number. To obtain more representative values, the burdens are calculated integrating the aerosol concentrations over\n275\nthe domains relevant to the respective sector. For land transport and shipping, the two lowermost model layers (from the surface\nto an altitude of about 250 m) are considered, over the continents and over the oceans, respectively. Aviation concentrations are\nintegrated globally at the typical cruise altitudes between about 8 and 12 km. This approach is consistent with the one adopted\nin R13, thus allowing for a direct comparison of the results. In the following, we analyse the impact of the emissions from the three sectors on the burdens and concentrations of relevant aerosol species, for the present-day (2015) and the three SSP\n280\nscenarios in 2030 and 2050. We also compare with the results for the year 2000 obtained with the CMIP5 inventory assessed\nby R13. For a correct interpretation of the concentration values discussed in the following, it should be noted that these are\nlarge-scale mean concentrations, which are smaller than the peak concentrations occurring close to the emission sources (e.g.,\nat kerbside). Such peak values are highly relevant for assessing air pollution effects, but cannot be resolved by large-scale\nclimate models which rely on mean concentrations driving large-scale climate effects. 285 the three sectors on the burdens and concentrations of relevant aerosol species, for the present-day (2015) and the three SSP\n280\nscenarios in 2030 and 2050. We also compare with the results for the year 2000 obtained with the CMIP5 inventory assessed\nby R13. For a correct interpretation of the concentration values discussed in the following, it should be noted that these are\nlarge-scale mean concentrations, which are smaller than the peak concentrations occurring close to the emission sources (e.g.,\nat kerbside). Such peak values are highly relevant for assessing air pollution effects, but cannot be resolved by large-scale\nclimate models which rely on mean concentrations driving large-scale climate effects. 4\nTransport impact on aerosol concentrations 285 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n1.8\n2.9\n2.7\n2.5\n1.3\n0.2\n2.1\n1.6\n2.1\n6.3\n2.2\n0.02\n0.8\n0.3\n0.5\n3.3\n1.1\n0.005\n1.8\n1.0\n0.9\n6.1\n1.9\n0.01\n2.4\n1.7\n2.2\n8.0\n2.8\n0.02\n0.2\n0.09\n0.2\n1.1\n0.3\n0.001\n1.7\n1.0\n0.8\n7.8\n2.4\n0.01\n2.9\n2.0\n2.1\n9.0\n3.1\n0.03\n(a) - Land transport (absolute impact)\n-2\n0\n2\n4\n6\n8\n10\n12\nmass [Gg] / number [1026 particles]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n0.2\n0.3\n9.4\n1.1\n1.0\n0.01\n0.3\n0.3\n6.3\n6.9\n1.6\n0.003\n0.2\n0.2\n2.4\n4.9\n0.6\n0.001\n0.2\n0.3\n2.9\n6.5\n0.7\n0.001\n0.2\n0.3\n4.8\n6.0\n1.2\n0.002\n0.04\n0.2\n0.4\n0.5\n0.08\n0.001\n0.03\n0.03\n1.5\n5.9\n0.4\n-0.001\n0.1\n0.2\n2.9\n4.1\n0.7\n0.001\n(b) - Shipping (absolute impact)\n-2\n0\n2\n4\n6\n8\n10\n12\nmass [Gg] / number [1026 particles]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n0.1\n0.000\n3.2\n-1.1\n-0.2\n12.3\n0.3\n1.0\n7.1\n0.1\n1.7\n1.1\n0.1\n0.4\n5.2\n0.7\n1.7\n1.3\n0.3\n1.2\n8.9\n1.7\n2.8\n1.5\n0.4\n1.4\n10.0\n0.1\n2.5\n1.5\n0.09\n0.6\n5.8\n2.1\n2.3\n1.4\n0.3\n1.2\n10.4\n4.5\n4.1\n1.9\n0.5\n1.6\n11.9\n0.8\n3.3\n1.7\n(c) - Aviation (absolute impact)\n-2\n0\n2\n4\n6\n8\n10\n12\nmass [Gg] / number [1026 particles]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n19.5\n3.5\n3.5\n28.6\n4.8\n12.8\n18.5\n2.2\n3.0\n24.6\n8.1\n9.1\n16.3\n0.6\n1.6\n17.0\n6.9\n3.4\n19.4\n1.5\n1.4\n22.9\n7.0\n7.4\n19.3\n2.2\n3.2\n25.6\n9.2\n10.7\n7.6\n0.2\n0.7\n7.7\n3.3\n1.0\n23.8\n1.6\n1.6\n29.0\n11.0\n7.9\n21.9\n2.5\n3.0\n26.2\n9.4\n11.9\n(d) - Land transport (relative contribution)\n-5\n0\n5\n10\n15\n20\n25\n30\n[%]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n6.0\n1.4\n13.6\n24.0\n6.9\n3.3\n4.8\n1.0\n9.0\n24.5\n8.3\n1.5\n6.8\n1.1\n5.8\n23.5\n5.9\n0.8\n5.0\n1.0\n4.5\n24.0\n4.2\n0.8\n3.5\n0.8\n6.8\n20.1\n6.2\n1.1\n2.7\n1.1\n1.2\n3.7\n1.1\n0.9\n0.9\n0.1\n2.9\n23.0\n2.9\n-0.4\n2.5\n0.7\n4.0\n13.9\n3.6\n0.8\n(e) - Shipping (relative contribution)\n-5\n0\n5\n10\n15\n20\n25\n30\n[%]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n1.8\n0.000\n2.9\n-4.8\n-0.6\n11.5\n4.6\n2.1\n4.3\n0.1\n2.6\n6.2\n4.1\n1.0\n4.7\n0.6\n2.7\n8.9\n5.8\n2.5\n5.7\n2.0\n4.0\n8.7\n5.8\n2.7\n5.9\n0.2\n3.5\n8.4\n4.1\n1.8\n5.5\n1.6\n3.5\n9.2\n6.8\n2.7\n7.8\n3.8\n5.7\n12.0\n6.6\n3.1\n6.8\n0.8\n4.3\n9.7\n(f) - Aviation (relative contribution)\n-5\n0\n5\n10\n15\n20\n25\n30\n[%]\nFigure 2. 4\nTransport impact on aerosol concentrations Absolute impact (a-c) and relative contribution (d-f) of the three transport sectors to the burdens of various aerosol species and o\nparticle number. Changes which are not statistically significant at a 95% confidence level are shown in gray. Note the different units for mass\n(Gg) and number burdens (1026 particles) in panels (a-c). 4\nTransport impact on aerosol concentrations 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n1.8\n2.9\n2.7\n2.5\n1.3\n0.2\n2.1\n1.6\n2.1\n6.3\n2.2\n0.02\n0.8\n0.3\n0.5\n3.3\n1.1\n0.005\n1.8\n1.0\n0.9\n6.1\n1.9\n0.01\n2.4\n1.7\n2.2\n8.0\n2.8\n0.02\n0.2\n0.09\n0.2\n1.1\n0.3\n0.001\n1.7\n1.0\n0.8\n7.8\n2.4\n0.01\n2.9\n2.0\n2.1\n9.0\n3.1\n0.03\n(a) - Land transport (absolute impact)\n-2\n0\n2\n4\n6\n8\n10\n12\nmass [Gg] / number [1026 particles]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n0.2\n0.3\n9.4\n1.1\n1.0\n0.01\n0.3\n0.3\n6.3\n6.9\n1.6\n0.003\n0.2\n0.2\n2.4\n4.9\n0.6\n0.001\n0.2\n0.3\n2.9\n6.5\n0.7\n0.001\n0.2\n0.3\n4.8\n6.0\n1.2\n0.002\n0.04\n0.2\n0.4\n0.5\n0.08\n0.001\n0.03\n0.03\n1.5\n5.9\n0.4\n-0.001\n0.1\n0.2\n2.9\n4.1\n0.7\n0.001\n(b) - Shipping (absolute impact)\n-2\n0\n2\n4\n6\n8\n10\n12\nmass [Gg] / number [1026 particles]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n0.1\n0.000\n3.2\n-1.1\n-0.2\n12.3\n0.3\n1.0\n7.1\n0.1\n1.7\n1.1\n0.1\n0.4\n5.2\n0.7\n1.7\n1.3\n0.3\n1.2\n8.9\n1.7\n2.8\n1.5\n0.4\n1.4\n10.0\n0.1\n2.5\n1.5\n0.09\n0.6\n5.8\n2.1\n2.3\n1.4\n0.3\n1.2\n10.4\n4.5\n4.1\n1.9\n0.5\n1.6\n11.9\n0.8\n3.3\n1.7\n(c) - Aviation (absolute impact)\n-2\n0\n2\n4\n6\n8\n10\n12\nmass [Gg] / number [1026 particles] 2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n19.5\n3.5\n3.5\n28.6\n4.8\n12.8\n18.5\n2.2\n3.0\n24.6\n8.1\n9.1\n16.3\n0.6\n1.6\n17.0\n6.9\n3.4\n19.4\n1.5\n1.4\n22.9\n7.0\n7.4\n19.3\n2.2\n3.2\n25.6\n9.2\n10.7\n7.6\n0.2\n0.7\n7.7\n3.3\n1.0\n23.8\n1.6\n1.6\n29.0\n11.0\n7.9\n21.9\n2.5\n3.0\n26.2\n9.4\n11.9\n(d) - Land transport (relative contribution)\n-5\n0\n5\n10\n15\n20\n25\n30\n[%]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n6.0\n1.4\n13.6\n24.0\n6.9\n3.3\n4.8\n1.0\n9.0\n24.5\n8.3\n1.5\n6.8\n1.1\n5.8\n23.5\n5.9\n0.8\n5.0\n1.0\n4.5\n24.0\n4.2\n0.8\n3.5\n0.8\n6.8\n20.1\n6.2\n1.1\n2.7\n1.1\n1.2\n3.7\n1.1\n0.9\n0.9\n0.1\n2.9\n23.0\n2.9\n-0.4\n2.5\n0.7\n4.0\n13.9\n3.6\n0.8\n(e) - Shipping (relative contribution)\n-5\n0\n5\n10\n15\n20\n25\n30\n[%]\n2000\nCMIP5\n2015\nCMIP6\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\nBC\nPOM\nSO4\nNO3\nNH4\nParticle\nnumber\n1.8\n0.000\n2.9\n-4.8\n-0.6\n11.5\n4.6\n2.1\n4.3\n0.1\n2.6\n6.2\n4.1\n1.0\n4.7\n0.6\n2.7\n8.9\n5.8\n2.5\n5.7\n2.0\n4.0\n8.7\n5.8\n2.7\n5.9\n0.2\n3.5\n8.4\n4.1\n1.8\n5.5\n1.6\n3.5\n9.2\n6.8\n2.7\n7.8\n3.8\n5.7\n12.0\n6.6\n3.1\n6.8\n0.8\n4.3\n9.7\n(f) - Aviation (relative contribution)\n-5\n0\n5\n10\n15\n20\n25\n30\n[%] Figure 2. 4\nTransport impact on aerosol concentrations Absolute impact (a-c) and relative contribution (d-f) of the three transport sectors to the burdens of various aerosol species and of\nparticle number. Changes which are not statistically significant at a 95% confidence level are shown in gray. Note the different units for mass\n(Gg) and number burdens (1026 particles) in panels (a-c). Figure 2. Absolute impact (a-c) and relative contribution (d-f) of the three transport sectors to the burdens of various aerosol species and of\nparticle number. Changes which are not statistically significant at a 95% confidence level are shown in gray. Note the different units for mass\n(Gg) and number burdens (1026 particles) in panels (a-c). 11 11 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.1\nLand transport The contribution of land transport to present-day aerosol burdens is particularly large for BC and aerosol nitrate (NO3), see\nFig. 2a. The continental near-surface burden of BC is 2.1 Gg in 2015, contributing 19% of the total, similar to the value for\nthe year 2000. The nitrate burden from land transport, on the other hand, is larger in 2015 than in R13 (6.3 vs. 2.5 Gg), but\nits relative contribution is slightly lower (25% vs. 29%). This is consistent with the difference in NOx (a nitrate precursor)\n90\nemissions between CMIP5 and CMIP6 (Fig. 1a). The contribution of land transport to present-day aerosol burdens is particularly large for BC and aerosol nitrate (NO3), see\nFig. 2a. The continental near-surface burden of BC is 2.1 Gg in 2015, contributing 19% of the total, similar to the value for\nthe year 2000. The nitrate burden from land transport, on the other hand, is larger in 2015 than in R13 (6.3 vs. 2.5 Gg), but\nits relative contribution is slightly lower (25% vs. 29%). This is consistent with the difference in NOx (a nitrate precursor)\n290\nemissions between CMIP5 and CMIP6 (Fig. 1a). In the future scenarios, the absolute impact of land transport on BC is strongly reduced in SSP1 and SSP2 in 2030 and\nfurther in 2050, but only in SSP1 this corresponds to a reduction in the relative contributions. On the one hand, this shows\nthat the optimistic SSP1 scenario implements stringent air quality control policies that can effectively reduce land-transport- 290 In the future scenarios, the absolute impact of land transport on BC is strongly reduced in SSP1 and SSP2 in 2030 and\nfurther in 2050, but only in SSP1 this corresponds to a reduction in the relative contributions. On the one hand, this shows\nthat the optimistic SSP1 scenario implements stringent air quality control policies that can effectively reduce land-transport- induced near-surface concentrations of BC. On the other hand, the higher degree of electrification of this sector in SSP1 (van\n295\nVuuren et al., 2017, up to 75%) compared to SSP2 (Fricko et al., 2017, up to 50%) implies strong reductions of BC from land\ntransport, but not from other sectors, hence decreasing the relative contribution of land transport itself. Similar conclusions\nhold for nitrate, where again only SSP1 is successful in decreasing the land-transport-induced burden both in absolute and\nrelative term, with a very large decrease in 2050. 4.1\nLand transport This is reduced in SSP1 and SSP2 in 2030 and 2050, while it keeps increasing Transport-induced changes in particle number burden are relevant as well, not only for air quality, but also for climate,\n300\nsince aerosol particles can act as cloud condensation nuclei in warm clouds if they grow to a sufficient size. An increase in their\nconcentration can therefore lead to a change in the microphysical and radiative properties of these clouds (Boucher et al., 2013). Land transport provides a significant contribution to particle number, with 9% of near-surface particle number burden over the\ncontinents attributable to this sector in 2015. This is reduced in SSP1 and SSP2 in 2030 and 2050, while it keeps increasing in SSP3. Note that the impacts on land-transport-induced particle number concentrations is strongly related to the assumptions\n305\non the size distribution of emitted particles. As shown in R13, assuming size distribution parameters representative of either\nyoung or aged particle populations upon emissions can change the resulting impacts on number concentration by about one\norder of magnitude. As discussed in Sect. 1 and Table 1, here we assume an aged distribution for particles emitted by land\ntransport, representative of air masses on large spatial scales and therefore more consistent with the representation in the coarse in SSP3. Note that the impacts on land-transport-induced particle number concentrations is strongly related to the assumptions\n305\non the size distribution of emitted particles. As shown in R13, assuming size distribution parameters representative of either\nyoung or aged particle populations upon emissions can change the resulting impacts on number concentration by about one\norder of magnitude. As discussed in Sect. 1 and Table 1, here we assume an aged distribution for particles emitted by land\ntransport, representative of air masses on large spatial scales and therefore more consistent with the representation in the coarse resolution of a global model. 310\nThe geographical distribution of land-transport-induced surface-level BC concentration is shown in Fig. 3. In 2015 (Fig. 3b),\nthe largest absolute impacts are found over China, India and the Arabian Peninsula, with concentrations changes of about\n1 µg m−3. Large values are also simulated over Eastern Europe and in some spots in South Africa and South America, in cor-\nrespondence of the largest urban areas. Western Europe and Eastern U.S. are characterized by generally lower land-transport- resolution of a global model. 4.1\nLand transport induced near-surface concentrations of BC. On the other hand, the higher degree of electrification of this sector in SSP1 (van\n295\nVuuren et al., 2017, up to 75%) compared to SSP2 (Fricko et al., 2017, up to 50%) implies strong reductions of BC from land\ntransport, but not from other sectors, hence decreasing the relative contribution of land transport itself. Similar conclusions\nhold for nitrate, where again only SSP1 is successful in decreasing the land-transport-induced burden both in absolute and\nrelative term, with a very large decrease in 2050. induced near-surface concentrations of BC. On the other hand, the higher degree of electrification of this sector in SSP1 (van\n295\nVuuren et al., 2017, up to 75%) compared to SSP2 (Fricko et al., 2017, up to 50%) implies strong reductions of BC from land\ntransport, but not from other sectors, hence decreasing the relative contribution of land transport itself. Similar conclusions\nhold for nitrate, where again only SSP1 is successful in decreasing the land-transport-induced burden both in absolute and\nrelative term, with a very large decrease in 2050. Transport-induced changes in particle number burden are relevant as well, not only for air quality, but also for climate,\n300\nsince aerosol particles can act as cloud condensation nuclei in warm clouds if they grow to a sufficient size. An increase in their\nconcentration can therefore lead to a change in the microphysical and radiative properties of these clouds (Boucher et al., 2013). Land transport provides a significant contribution to particle number, with 9% of near-surface particle number burden over the\ncontinents attributable to this sector in 2015. This is reduced in SSP1 and SSP2 in 2030 and 2050, while it keeps increasing Transport-induced changes in particle number burden are relevant as well, not only for air quality, but also for climate,\n300\nsince aerosol particles can act as cloud condensation nuclei in warm clouds if they grow to a sufficient size. An increase in their\nconcentration can therefore lead to a change in the microphysical and radiative properties of these clouds (Boucher et al., 2013). Land transport provides a significant contribution to particle number, with 9% of near-surface particle number burden over the\ncontinents attributable to this sector in 2015. 4.1\nLand transport 310\nThe geographical distribution of land-transport-induced surface-level BC concentration is shown in Fig. 3. In 2015 (Fig. 3b),\nthe largest absolute impacts are found over China, India and the Arabian Peninsula, with concentrations changes of about\n1 µg m−3. Large values are also simulated over Eastern Europe and in some spots in South Africa and South America, in cor-\nrespondence of the largest urban areas. Western Europe and Eastern U.S. are characterized by generally lower land-transport- induced BC concentrations. This pattern roughly matches the results for 2000 with the CMIP5 inventory (R13), although with\n315\nlarger values, especially in the Middle East and in Eastern and Southern Asia. In relative terms (Fig. 3c), however, the contri-\nbution of land transport is confirmed to be relatively small in Eastern and Southern Asia, amounting to only 10 to 20% of the\nsurface-level BC concentration in these countries, due to the prevalence of emissions from other sectors, like energy production\nor household. A somewhat opposite effect is the large relative contribution over the Arabian Peninsula, with 60–70% of BC 12 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.1\nLand transport 2015\na\nBackground concentration\nb\nAbsolute impact\nc\nRelative contribution\n0.005\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on BC\n2030 SSP1\nd\nBackground concentration\ne\nAbsolute impact\nf\nRelative contribution\n0.005\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on BC\n2030 SSP2\ng\nBackground concentration\nh\nAbsolute impact\ni\nRelative contribution\n0.005\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on BC\n2030 SSP3\nj\nBackground concentration\nk\nAbsolute impact\nl\nRelative contribution\n0.005\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on BC\n2050 SSP1\nm\nBackground concentration\nn\nAbsolute impact\no\nRelative contribution\n0.005\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on BC\n2050 SSP2\np\nBackground concentration\nq\nAbsolute impact\nr\nRelative contribution\n0.005\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on BC\n2050 SSP3\ns\nBackground concentration\nt\nAbsolute impact\nu\nRelative contribution\n0.005\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on BC\nLand transport impact on surface-level BC concentration. 4.1\nLand transport The left panel show the background concentration for r\nd right panels show the land-transport-induced absolute impacts (Eq. 1) and the relative contributions (Eq. 2), resp\nre the impacts are not significant to a 95% confidence level are masked out in gray. Absolute impact\nLand transport impact on BC [%] Figure 3. Land transport impact on surface-level BC concentration. The left panel show the background concentration for reference. The\nmiddle and right panels show the land-transport-induced absolute impacts (Eq. 1) and the relative contributions (Eq. 2), respectively. Grid\npoints where the impacts are not significant to a 95% confidence level are masked out in gray. 13 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.1\nLand transport 2015\na\nBackground concentration\nb\nAbsolute impact\nc\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-2000\n-1000\n-500\n-200\n-100\n -50\n -20\n 0\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on particle number\n2030 SSP1\nd\nBackground concentration\ne\nAbsolute impact\nf\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-2000\n-1000\n-500\n-200\n-100\n -50\n -20\n 0\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on particle number\n2030 SSP2\ng\nBackground concentration\nh\nAbsolute impact\ni\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-2000\n-1000\n-500\n-200\n-100\n -50\n -20\n 0\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on particle number\n2030 SSP3\nj\nBackground concentration\nk\nAbsolute impact\nl\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-2000\n-1000\n-500\n-200\n-100\n -50\n -20\n 0\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on particle number\n2050 SSP1\nm\nBackground concentration\nn\nAbsolute impact\no\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-2000\n-1000\n-500\n-200\n-100\n -50\n -20\n 0\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on particle number\n2050 SSP2\np\nBackground concentration\nq\nAbsolute impact\nr\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-2000\n-1000\n-500\n-200\n-100\n -50\n -20\n 0\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on particle number\n2050 SSP3\ns\nBackground concentration\nt\nAbsolute impact\nu\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-2000\n-1000\n-500\n-200\n-100\n -50\n -20\n 0\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nLand transport impact on particle number\n4. 4.1\nLand transport S3), which could be related to an enhanced aerosol removal via wet deposition. This effect, however, is consistent with the storyline of this scenario, assuming large differences between developed and developing countries. 335\nSimilar conclusions can be drawn for the other species (Figs. S1–S6), although in relative terms only nitrate (Fig. S1) and\nparticle number concentrations (Fig. 4) show substantial impacts from land transport in 2015 and in the future projections. A remarkable feature is the land-transport-induced decrease in surface-level aerosol sulfate concentration over some parts of\nIndia and China (Fig. S3), which could be related to an enhanced aerosol removal via wet deposition. This effect, however, is small in relative terms (< 10%). 340\nWith respect to the RCPs scenarios, the projections for the land-transport sectors in the SSPs appear to be more coherent\nwith their narratives in terms of short-lived pollutants, while this was not always the case in the RCPs. Righi et al. (2015),\nfor instance, found that the RCP scenarios with the most stringent climate policies were often characterized by larger impacts\non aerosol concentrations, hence showing an anti-correlation between their ranking and the resulting impact on short-lived species. This is not the case in the SSP scenarios: SSP1 is by far the greenest scenario in terms of air pollution impact, while\n345\nSSP2 and SSP3 show only limited changes in 2030 and 2050 with respect to the present-day situation, consistently with their\nunderlying storylines and long-term climate projection. species. This is not the case in the SSP scenarios: SSP1 is by far the greenest scenario in terms of air pollution impact, while\n345\nSSP2 and SSP3 show only limited changes in 2030 and 2050 with respect to the present-day situation, consistently with their\nunderlying storylines and long-term climate projection. 4.1\nLand transport As in Fig. 3, but for the land transport impact on surface-level particle number concentration. Absolute impact\nLand transport impact on particle number Figure 4. As in Fig. 3, but for the land transport impact on surface-level particle number concentration. concentration due to land-transport in these regions, following the comparatively low BC emissions from other sectors in this\n320\nregion. This sector remains quite relevant for air pollution also in the developed countries, with contributions of 30 to 50% in\nEurope and in the U.S. These numbers are lower than in 2000, also in terms of absolute land-transport-induced concentration 14 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. changes, revealing the effectiveness of the air pollution control measures and the technical improvements in this sector over\nthe 15 years. Fig. 3c also reveals the importance of the long-range transport of polluted air masses from the continents to the\noceans, especially in the subtropics and southern mid-latitudes. 325 changes, revealing the effectiveness of the air pollution control measures and the technical improvements in this sector over\nthe 15 years. Fig. 3c also reveals the importance of the long-range transport of polluted air masses from the continents to the oceans, especially in the subtropics and southern mid-latitudes. 325\nAs expected, the land-transport-induced BC concentrations are strongly reduced in SSP1 in 2030 (Fig. 3e) and become\nalmost negligible in 2050 (Fig. 3n), with only India and the Arabian Peninsula showing concentration changes larger than\n0.05 µg m−3, although the relative contributions in the Arabian Peninsula still remain high (Fig. 3f,o). This is consistent with\nthe narrative of this scenario, considering a worldwide transition to a more sustainable future with reduced inequality across oceans, especially in the subtropics and southern mid-latitudes. 325\nAs expected, the land-transport-induced BC concentrations are strongly reduced in SSP1 in 2030 (Fig. 3e) and become\nalmost negligible in 2050 (Fig. 3n), with only India and the Arabian Peninsula showing concentration changes larger than\n0.05 µg m−3, although the relative contributions in the Arabian Peninsula still remain high (Fig. 3f,o). This is consistent with\nthe narrative of this scenario, considering a worldwide transition to a more sustainable future with reduced inequality across the narrative of this scenario, considering a worldwide transition to a more sustainable future with reduced inequality across\ncountries. 4.1\nLand transport In SSP2, on the contrary, only the developed countries show a noticeable decrease in BC concentration in 2030\n330\nand 2050, both in absolute and relative terms, while developing countries remain on a similar level as in 2015 (Fig. 3h,i). In\n2050, land transport is still the major source of BC pollution in many regions of the world, including the Arabian Peninsula,\nNorth Africa, and Central and South America (Fig. 3k,l). SSP3 shows no remarkable differences with respect to SSP2 in 2030\n(Fig. 3q,r), whereas it is characterized by an increased land-transport-induced concentration in 2050 (Fig. 3t,u), which is again\ni\ni h h\nli\nf hi\ni\ni\nl\ndiff\nb\nd\nl\nd\nd d\nl\ni\ni\n335 countries. In SSP2, on the contrary, only the developed countries show a noticeable decrease in BC concentration in 2030\n330\nand 2050, both in absolute and relative terms, while developing countries remain on a similar level as in 2015 (Fig. 3h,i). In\n2050, land transport is still the major source of BC pollution in many regions of the world, including the Arabian Peninsula,\nNorth Africa, and Central and South America (Fig. 3k,l). SSP3 shows no remarkable differences with respect to SSP2 in 2030\n(Fig. 3q,r), whereas it is characterized by an increased land-transport-induced concentration in 2050 (Fig. 3t,u), which is again countries. In SSP2, on the contrary, only the developed countries show a noticeable decrease in BC concentration in 2030\n330\nand 2050, both in absolute and relative terms, while developing countries remain on a similar level as in 2015 (Fig. 3h,i). In\n2050, land transport is still the major source of BC pollution in many regions of the world, including the Arabian Peninsula,\nNorth Africa, and Central and South America (Fig. 3k,l). SSP3 shows no remarkable differences with respect to SSP2 in 2030\n(Fig. 3q,r), whereas it is characterized by an increased land-transport-induced concentration in 2050 (Fig. 3t,u), which is again consistent with the storyline of this scenario, assuming large differences between developed and developing countries. 335\nSimilar conclusions can be drawn for the other species (Figs. S1–S6), although in relative terms only nitrate (Fig. S1) and\nparticle number concentrations (Fig. 4) show substantial impacts from land transport in 2015 and in the future projections. A remarkable feature is the land-transport-induced decrease in surface-level aerosol sulfate concentration over some parts of\nIndia and China (Fig. 4.2\nShipping efficient take up of ultra-fine particles by larger aerosol in ship plumes, which occurs within a time scale of the order of only\n365\n1 hour (Petzold et al., 2008; Righi et al., 2011). This process is implicitly considered in our assumptions on the size distribution\nof ship-emitted particles (see Sect. 1 and Table 1), with 90% of shipping emissions released in the accumulation mode, and\nleads to the small impacts on particle burden. These are further reduced in the future scenarios, in line with the reductions in\nthe mass burden of the most relevant species from this sector. Fig. 5 depicts the geographical distributions of shipping-induced near-surface sulfate concentrations. In 2015 (Fig. 5b), the\n370\nlargest ship-induced concentrations are simulated over the Northern Hemisphere, especially in the Eastern Atlantic Ocean, in\nthe Northern Indian Ocean and in the Mediterranean, with values around 1 µg m−3. Significant changes of 0.05–0.1 µg m−3\nare also simulated over land in many coastal regions of the Northern Hemisphere, confirming the relevance of this sector for\ncontinental air pollution (Eyring et al., 2010). This pattern closely matches the distribution simulated in R13 for the year 2000. Fig. 5 depicts the geographical distributions of shipping-induced near-surface sulfate concentrations. In 2015 (Fig. 5b), the\n370\nlargest ship-induced concentrations are simulated over the Northern Hemisphere, especially in the Eastern Atlantic Ocean, in\nthe Northern Indian Ocean and in the Mediterranean, with values around 1 µg m−3. Significant changes of 0.05–0.1 µg m−3\nare also simulated over land in many coastal regions of the Northern Hemisphere, confirming the relevance of this sector for\ncontinental air pollution (Eyring et al., 2010). This pattern closely matches the distribution simulated in R13 for the year 2000. Fig. 5 depicts the geographical distributions of shipping-induced near-surface sulfate concentrations. In 2015 (Fig. 5b), the\n370\nlargest ship-induced concentrations are simulated over the Northern Hemisphere, especially in the Eastern Atlantic Ocean, in\nthe Northern Indian Ocean and in the Mediterranean, with values around 1 µg m−3. Significant changes of 0.05–0.1 µg m−3\nare also simulated over land in many coastal regions of the Northern Hemisphere, confirming the relevance of this sector for\ncontinental air pollution (Eyring et al., 2010). This pattern closely matches the distribution simulated in R13 for the year 2000. The relative contribution of shipping to surface-level sulfate concentration (Fig. 5c), however, is generally lower than in R13. 4.2\nShipping Such a low impact is a consequence of the very\nefficient take up of ultra-fine particles by larger aerosol in ship plumes, which occurs within a time scale of the order of only\n365\n1 hour (Petzold et al., 2008; Righi et al., 2011). This process is implicitly considered in our assumptions on the size distribution\nof ship-emitted particles (see Sect. 1 and Table 1), with 90% of shipping emissions released in the accumulation mode, and\nleads to the small impacts on particle burden. These are further reduced in the future scenarios, in line with the reductions in\nthe mass burden of the most relevant species from this sector. which are all characterized by a reduced impact of sulfate burden. In 2030, the relative contribution of shipping to near-surface\noceanic sulfate burden is reduced to 6–7% in all scenarios, and further to 1–4% in 2050, with the largest (smallest) decrease\nin SSP1 (SSP3), hence consistent with the scenario narratives. MARPOL also introduced emission limits on NOx for all new\nship engines built after year 2000 and above a certain power output. These regulations, however, seem to have a limited impact\n360\non the resulting nitrate burdens, which are only slightly reduced in 2030 for all scenarios. Stronger reductions in near-surface\nnitrate burden become effective in 2050, especially in SSP1 (4%) and SSP3 (14%), while they remain high (23%) in SSP2,\nconsistent with the high share of shipping NOx emissions in this scenario (Fig. 1). The shipping impact on particle number\nburden is quite small, about 1.5% in 2015, in line with the results of R13. Such a low impact is a consequence of the very\nefficient take up of ultra-fine particles by larger aerosol in ship plumes which occurs within a time scale of the order of only\n365 360 efficient take up of ultra-fine particles by larger aerosol in ship plumes, which occurs within a time scale of the order of only\n365\n1 hour (Petzold et al., 2008; Righi et al., 2011). This process is implicitly considered in our assumptions on the size distribution\nof ship-emitted particles (see Sect. 1 and Table 1), with 90% of shipping emissions released in the accumulation mode, and\nleads to the small impacts on particle burden. These are further reduced in the future scenarios, in line with the reductions in\nthe mass burden of the most relevant species from this sector. 4.2\nShipping As in the year 2000, the shipping sector is characterized by its large contributions to the burdens of nitrate and sulfate (SO4, see As in the year 2000, the shipping sector is characterized by its large contributions to the burdens As in the year 2000, the shipping sector is characterized by its large contributions to the burdens of nitrate and sulfate (SO4, see Fig. 2b) over the oceans. The impact of shipping on near-surface nitrate burden over the oceans in 2015 (6.9 Gg) is significantly\n350\nlarger than in R13 for the year 2000 (1.1 Gg), but it remains stable in relative terms at about 25%. Sulfate pollution, on the other\nhand, is smaller than in 2000 (6.3 vs. 9.4 Gg) and its relative contribution is reduced to 9% compared to the 14% value reported\nin R13. As mentioned above, this can be related to the introduction of the MARPOL Annex VI regulations on the maximum\nFSC in shipping. Although fully implemented in 2020, a first step towards the reduction was taken in 2012, reducing the maximum allowed FSC by mass from 4.5% to 3.5% globally (Buhaug et al., 2009). Stricter limits were applied in the SECAs,\n355\nreducing the cap from 1.5% to 1% in 2010 and to 0.1% in 2015. This reduction trend appears to continue in the future scenarios, 15 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. which are all characterized by a reduced impact of sulfate burden. In 2030, the relative contribution of shipping to near-surface\noceanic sulfate burden is reduced to 6–7% in all scenarios, and further to 1–4% in 2050, with the largest (smallest) decrease\nin SSP1 (SSP3), hence consistent with the scenario narratives. MARPOL also introduced emission limits on NOx for all new\nship engines built after year 2000 and above a certain power output. These regulations, however, seem to have a limited impact\n360\non the resulting nitrate burdens, which are only slightly reduced in 2030 for all scenarios. Stronger reductions in near-surface\nnitrate burden become effective in 2050, especially in SSP1 (4%) and SSP3 (14%), while they remain high (23%) in SSP2,\nconsistent with the high share of shipping NOx emissions in this scenario (Fig. 1). The shipping impact on particle number\nburden is quite small, about 1.5% in 2015, in line with the results of R13. 4.2\nShipping 375\nIn particular, lower contributions are found over the Northern Pacific Ocean (20–40%, due to the lower absolute impact of\nshipping in this region than in R13) and over the Southern Atlantic (10–30%, due to the higher background concentration of\nsulfate in this region than in R13). An interesting feature is the large contribution of shipping to the sulfate concentration over\nthe Northern Atlantic Ocean, between the coasts of Greenland and Scandinavia: this is due to the increased emissions in the polar regions in the CMIP6 dataset for 2015 compared to the CMIP5 for 2000, in particular along the Northern Sea Route,\n380\nwhich has become more travelled in recent years following the depletion of the Arctic ice. The shipping impacts are reduced in 2030 in all scenarios, although the geographical distribution remains essentially the\nsame. This is because the SSPs do not consider any significant change in the route displacement, but just apply a scaling of\nthe traffic activity across the existing routes according to a global spatial matrix (Feng et al., 2020). SSP1 and SSP2 (Fig. 5e,h) project similar ship-induced concentrations, with the highest values reduced to 0.2–0.5 µg m−3. In relative terms, however,\n385\nthis translates in a higher impact in SSP1, due to the cleaner background following the reductions in the emissions from other\nanthropogenic sectors. In SSP3 (Fig. 5k), the concentration changes in 2030 are still relatively high compared to 2015, although\nwith a smaller relative contribution (Fig. 5l). The shipping sector has only a negligible impact on sulfate pollution in 2050 in\nSSP1 and SSP2 (Fig. 5n,o,q,r), while it remains relevant in SSP3, with large induced concentrations, around 0.2–0.5 µg m−3 in the Northern Indian Ocean and around the Mediterranean, but with a relative contribution reduced to a maximum of 20%\n390\nover the Northern Atlantic and Northern Pacific Ocean (Fig. 5t,u). 16 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.2\nShipping 2015\na\nBackground concentration\nb\nAbsolute impact\nc\nRelative contribution\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n 10\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n-0.01\n 0\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on SO4\n2030 SSP1\nd\nBackground concentration\ne\nAbsolute impact\nf\nRelative contribution\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n 10\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n-0.01\n 0\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on SO4\n2030 SSP2\ng\nBackground concentration\nh\nAbsolute impact\ni\nRelative contribution\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n 10\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n-0.01\n 0\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on SO4\n2030 SSP3\nj\nBackground concentration\nk\nAbsolute impact\nl\nRelative contribution\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n 10\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n-0.01\n 0\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on SO4\n2050 SSP1\nm\nBackground concentration\nn\nAbsolute impact\no\nRelative contribution\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n 10\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n-0.01\n 0\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on SO4\n2050 SSP2\np\nBackground concentration\nq\nAbsolute impact\nr\nRelative contribution\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n 10\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n-0.01\n 0\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on SO4\n2050 SSP3\ns\nBackground concentration\nt\nAbsolute impact\nu\nRelative contribution\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n 5\n 10\n[ g m\n3]\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n-0.01\n 0\n0.01\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n[ g m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on SO4\nFigure 5. 4.2\nShipping As in Fig. 3, but for the shipping impact on surface-level SO4 concentration. Absolute impact\nShipping impact on SO4 Figure 5. As in Fig. 3, but for the shipping impact on surface-level SO4 concentration. The impact on nitrate (Fig. S7) shows a similar geographic distribution, but a much different future evolution. The shipping-\ninduced nitrate concentrations remain considerably high through 2030 and 2050, with very large relative contributions around\nand above 70% over most of the northern hemispheric oceans and along the major southern hemispheric routes. This reveals the 17 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.2\nShipping Absolute impact\nShipping impact on particle number 2015\na\nBackground concentration\nb\nAbsolute impact\nc\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-100\n -50\n -20\n -10\n -5\n -2\n 0\n 2\n 5\n 10\n 20\n 50\n 100\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on particle number\n2030 SSP1\nd\nBackground concentration\ne\nAbsolute impact\nf\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-100\n -50\n -20\n -10\n -5\n -2\n 0\n 2\n 5\n 10\n 20\n 50\n 100\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on particle number\n2030 SSP2\ng\nBackground concentration\nh\nAbsolute impact\ni\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-100\n -50\n -20\n -10\n -5\n -2\n 0\n 2\n 5\n 10\n 20\n 50\n 100\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on particle number\n2030 SSP3\nj\nBackground concentration\nk\nAbsolute impact\nl\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-100\n -50\n -20\n -10\n -5\n -2\n 0\n 2\n 5\n 10\n 20\n 50\n 100\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on particle number\n2050 SSP1\nm\nBackground concentration\nn\nAbsolute impact\no\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-100\n -50\n -20\n -10\n -5\n -2\n 0\n 2\n 5\n 10\n 20\n 50\n 100\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on particle number\n2050 SSP2\np\nBackground concentration\nq\nAbsolute impact\nr\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-100\n -50\n -20\n -10\n -5\n -2\n 0\n 2\n 5\n 10\n 20\n 50\n 100\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on particle number\n2050 SSP3\ns\nBackground concentration\nt\nAbsolute impact\nu\nRelative contribution\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n1000\n2000\n[cm\n3]\n-100\n -50\n -20\n -10\n -5\n -2\n 0\n 2\n 5\n 10\n 20\n 50\n 100\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nShipping impact on particle number\n6. 4.2\nShipping As in Fig. 3, but for the shipping impact on surface-level particle number concentration. -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nr\n \n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nu\nRelative contribution\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%] [cm\n3] Figure 6. As in Fig. 3, but for the shipping impact on surface-level particle number concentration. challenges in reducing the emissions from NOx in the shipping sectors, contrary to the sulfate which can be more effectively\n395\ncontrolled by reducing the shipping FSC. Nitrate impacts are effectively reduced only in the green SSP1 scenario in 2050. Another aspect to be taken into account is the competition between the formation pathways of ammonium sulfate and ammo- challenges in reducing the emissions from NOx in the shipping sectors, contrary to the sulfate which can be more effectively\n395\ncontrolled by reducing the shipping FSC. Nitrate impacts are effectively reduced only in the green SSP1 scenario in 2050. Another aspect to be taken into account is the competition between the formation pathways of ammonium sulfate and ammo- 18 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. nium nitrate for available ammonia in the lower troposphere, with ammonium sulfate formation being favoured. A reduction\nof sulfate concentration makes more ammonia available for the formation of ammonium nitrate, leading to enhanced nitrate\nformation, hence partly compensating the reduction in sulfate. Being the largest contributors to shipping pollution, sulfate and\n400\nnitrate also influence the impact of this sector on particle number concentrations (Fig. 6). Here again the largest impacts are\nfound over the northern hemispheric oceans and in the southern high latitude regions, progressively reducing in 2030 and 2050. Over the southern extra-tropics shipping induces a reduction of the particle number in most scenarios: this could be due to the\nrelatively large size of ship-induced particles inhibiting the nucleation of new particles typical of the pristine environments of\nthese regions and thus limiting or removing a key source of particle number. 405 4.3\nAviation Compared to the other two sectors, the impact of aviation on mass burdens is small (Fig. 2c), even though this is calculated\nin the altitude range of 8 to 12 km where most of the aviation emissions are released. Aviation contributes 0.3 Gg to the\nupper-tropospheric BC burden in 2015, almost three times more than in R13. The contribution is larger also in relative terms (5% versus 2%). This could be due to an increase in BC emissions between 2000 and 2015, but also to the improved model\n410\nvertical resolution adopted in the present study (41 levels) with respect to R13 (19 levels). The L41 resolution has been\nspecifically designed to resolve the upper troposphere and the related process in higher detail and has been shown to be\nsuitable in several modelling studies targeting this region (Dietmüller et al., 2014; Bock and Burkhardt, 2016; Righi et al.,\n2020). The coarse vertical resolution used in R13 may therefore have resulted in an underestimated impact of aviation-induced BC in the upper troposphere. This appears to be the case also for sulfate, which shows an aviation-induced upper-tropospheric\n415\nburden of 7.1 Gg, a factor of more than two larger than in R13. This is also larger in relative terms (4% vs. 3%). Despite the\nincreased aviation impact on the mass burden, the impact on number is lower by about one order of magnitude than in R13\n(1.1 × 1026 vs. 1.2 × 1027 particles) and about the half in relative terms (6% vs. 12%), although identical size distributions for\nthe emitted particles have been assumed in the two studies. Besides the aforementioned improvement in the vertical resolution, BC in the upper troposphere. This appears to be the case also for sulfate, which shows an aviation-induced upper-tropospheric\n415\nburden of 7.1 Gg, a factor of more than two larger than in R13. This is also larger in relative terms (4% vs. 3%). Despite the\nincreased aviation impact on the mass burden, the impact on number is lower by about one order of magnitude than in R13\n(1.1 × 1026 vs. 1.2 × 1027 particles) and about the half in relative terms (6% vs. 12%), although identical size distributions for\nthe emitted particles have been assumed in the two studies. Besides the aforementioned improvement in the vertical resolution, BC in the upper troposphere. 4.3\nAviation Aviation impact on zonally averaged BC concentration. The left panel show the background concentration for reference\nmiddle and right panels show the land-transport-induced absolute impacts and the relative contributions, respectively. Grid points whe\ndifferences are not significant at a 95% confidence level are masked out in gray Note that in contrast to the other figures the units\nhttps://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.3\nAviation -80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2015\na\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nb\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nc\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP1\nd\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ne\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nf\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP2\ng\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nh\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ni\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP3\nj\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nk\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nl\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP1\nm\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nn\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\no\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP2\np\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nq\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nr\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP3\ns\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nt\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nu\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\nAviation impact on zonally averaged BC concentration. 4.3\nAviation This appears to be the case also for sulfate, which shows an aviation-induced upper-tropospheric\n415\nburden of 7.1 Gg, a factor of more than two larger than in R13. This is also larger in relative terms (4% vs. 3%). Despite the\nincreased aviation impact on the mass burden, the impact on number is lower by about one order of magnitude than in R13\n(1.1 × 1026 vs. 1.2 × 1027 particles) and about the half in relative terms (6% vs. 12%), although identical size distributions for\nthe emitted particles have been assumed in the two studies. Besides the aforementioned improvement in the vertical resolution, the improved representation of the aerosol dynamics in the new version of the MADE aerosol submodel adopted here (MADE3;\n420\nKaiser et al., 2019) could explain these differences, as revealed, for instance, by the much lower background concentration of\naerosol particles (Fig. 8a) with respect to the previous assessment. The EMAC model system has also been updated with respect\nto the previous assessment and now uses the second version of the MESSy interface, comprising numerous update in several\nkey submodels, such as for example the scavenging submodel SCAV (Tost et al., 2006). Aviation-induced upper-tropospheric BC burden keeps growing until 2030 in SSP2 and SSP3, and further in 2050 for SSP3. Aviation-induced BC burden decreases\n425\nonly in the SSP1 scenarios, with a factor of more than two reduction in 2030 and further down in 2050. In relative terms\nthese changes are less marked, due to the corresponding changes in background BC concentrations. The sulfate burden follows\na similar path, with a weak decrease in SSP1 and a steady increase in SSP2 and SSP3. This shows that the a desulphurisation\nof the aviation sector is not considered as an emission reduction measure in these scenarios, although it is relatively easy to implement, as it does not require technical modifications nor does it need to cope with the long lifetime of the commercial fleet\n430\n(e.g. Unger, 2011; Moore et al., 2017). 4.3\nAviation 19 -80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2015\na\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nb\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nc\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP1\nd\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ne\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nf\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP2\ng\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nh\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ni\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP3\nj\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nk\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nl\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP1\nm\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nn\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\no\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP2\np\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nq\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nr\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP3\ns\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nt\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nu\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\nFigure 7. 4.3\nAviation Aviation impact on zonally averaged BC concentration. The left panel show the background concentration for referen\nmiddle and right panels show the land-transport-induced absolute impacts and the relative contributions, respectively. Grid points w\ndiff\nt i\nifi\nt t\n95%\nfid\nl\nl\nk d\nt i\nN t th t i\nt\nt t th\nth\nfi\nth\ni\nhttps://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.3\nAviation -80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2015\na\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nb\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nc\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP1\nd\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ne\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nf\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP2\ng\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nh\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ni\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP3\nj\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nk\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nl\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP1\nm\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nn\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\no\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP2\np\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nq\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nr\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP3\ns\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nt\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nu\nRelative contribution\n0.0001\n0.0002\n0.0005\n0.001\n0.002\n0.005\n0.01\n0.02\n0.05\n 0.1\n[ g m\n3]\n -2\n -1\n-0.5\n-0.2\n-0.1\n-0.05\n-0.02\n 0\n0.02\n0.05\n 0.1\n 0.2\n 0.5\n 1\n 2\n[ng m\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on BC\nFigure 7. 4.3\nAviation -80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2015\na\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nb\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nc\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP1\nd\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ne\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nf\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP2\ng\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nh\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ni\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP3\nj\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nk\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nl\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP1\nm\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nn\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\no\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP2\np\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nq\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nr\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP3\ns\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nt\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nu\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number -80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2015\na\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nb\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nc\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP1\nd\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ne\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nf\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP2\ng\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nh\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\ni\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2030 SSP3\nj\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nk\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nl\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP1\nm\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nn\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\no\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP2\np\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nq\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nr\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nPressure [hPa]\n2050 SSP3\ns\nBackground concentration\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nt\nAbsolute impact\n-80\n-60\n-40\n-20\n0\n20\n40\n60\n80\nLatitude [degrees]\n1000\n900\n800\n700\n600\n500\n400\n300\n200\n100\nu\nRelative contribution\n 100\n 200\n 300\n 400\n 500\n 600\n 700\n 800\n 900\n1000\n[cm\n3]\n-500\n-200\n-100\n -50\n -20\n -10\n -5\n 0\n 5\n 10\n 20\n 50\n 100\n 200\n 500\n[cm\n3]\n -70\n -60\n -50\n -40\n -30\n -20\n -10\n 0\n 10\n 20\n 30\n 40\n 50\n 60\n 70\n[%]\nAviation impact on particle number\nre 8. 4.3\nAviation The left panel show the background concentration for ref\nd right panels show the land-transport-induced absolute impacts and the relative contributions, respectively. Grid poin\ns are not significant at a 95% confidence level are masked out in gray. Note that, in contrast to the other figures, the\nhanges are nanogram per cubic meter. Figure 7. Aviation impact on zonally averaged BC concentration. The left panel show the background concentration for reference. The\nmiddle and right panels show the land-transport-induced absolute impacts and the relative contributions, respectively. Grid points where the\ndifferences are not significant at a 95% confidence level are masked out in gray. Note that, in contrast to the other figures, the units of the\nabsolute changes are nanogram per cubic meter. 20 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.3\nAviation 7h,q), while they increase in relative terms (Fig. 7i,l), up to\n50–60% in 2050. This is the scenario with the highest relative contribution in the future: even though SSP3 shows much higher\n3 overall pattern is still comparable. The future aviation impacts decrease only in the SSP1 scenario, while maintaining a similar\n440\nzonal distribution. Aviation-induced BC concentrations are reduced to 0.2–0.5 ng m−3 in 2030 and further down to 0.1 ng m−3\nover most of the northern latitudes in this scenario (Fig. 7e,n), with minor changes in relative terms (Fig. 7f,o). In SSP2, the\nabsolute impact remains approximately constant until 2050 (Fig. 7h,q), while they increase in relative terms (Fig. 7i,l), up to\n50–60% in 2050. This is the scenario with the highest relative contribution in the future: even though SSP3 shows much higher absolute impacts (around 1–2 ng m−3, Fig. 7k,t), the cleanest background emissions in SSP2 make the aviation sector more\n445\nimpacting in this scenario. Similar considerations can be made for the aviation-induced impacts on sulfate (Fig. S15), while the\nnitrate impacts (Fig. S13) show an interesting dual structure, with concentration increases between 5 and 20 ng m−3 throughout\nthe whole troposphere in northern mid-latitudes and significant increases in the upper troposphere at latitudes above 40◦N. As\nmentioned in Sect. 4.2, this is due to the competition between ammonium sulfate and ammonium nitrate for available ammonia, absolute impacts (around 1–2 ng m−3, Fig. 7k,t), the cleanest background emissions in SSP2 make the aviation sector more\n445\nimpacting in this scenario. Similar considerations can be made for the aviation-induced impacts on sulfate (Fig. S15), while the\nnitrate impacts (Fig. S13) show an interesting dual structure, with concentration increases between 5 and 20 ng m−3 throughout\nthe whole troposphere in northern mid-latitudes and significant increases in the upper troposphere at latitudes above 40◦N. As\nmentioned in Sect. 4.2, this is due to the competition between ammonium sulfate and ammonium nitrate for available ammonia, which is particularly distinct in the upper troposphere due to the low availability of ammonia (e.g. Unger et al., 2013). 450\nDespite the relatively small impact on mass concentrations, of the order of a few nanograms per cubic meter, the aviation\nsector significantly increase the particle number concentration in the upper troposphere. 4.3\nAviation As in Fig. 7, but for the aviation impact on zonally averaged particle number concentration. Figure 8. As in Fig. 7, but for the aviation impact on zonally averaged particle number concentration. The spatial distributions of aviation-induced BC concentrations are depicted in Fig. 7 as zonal mean maps. In 2015 (Fig. 7b),\nthe largest changes (0.5–1 ng m−3) are simulated for the Northern Hemisphere at latitudes > 40◦N at the typical cruise levels\nof 200–350 hPa. Similarly high concentrations are simulated at lower levels, possibly driven by short- and medium-range 21 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. flight over the continents, and close to the surface at mid-latitudes, related to landing and take-off and airport activities. The\n435\nSouthern Hemisphere concentrations are only weakly affected by aviation, with induced changes in BC concentrations below\n0.05 ng m−3. As inferred from the analysis of the burdens, these values are significantly higher as those reported in R13. This\nis also the case for the relative contribution of aviation to BC concentration (Fig. 7c), peaking at 30–40% at high latitude in the\nNorthern Hemisphere at 200–300 hPa. This is higher than the ∼5% value reported in R13 and shifted northward, although the flight over the continents, and close to the surface at mid-latitudes, related to landing and take-off and airport activities. The\n435\nSouthern Hemisphere concentrations are only weakly affected by aviation, with induced changes in BC concentrations below\n0.05 ng m−3. As inferred from the analysis of the burdens, these values are significantly higher as those reported in R13. This\nis also the case for the relative contribution of aviation to BC concentration (Fig. 7c), peaking at 30–40% at high latitude in the\nNorthern Hemisphere at 200–300 hPa. This is higher than the ∼5% value reported in R13 and shifted northward, although the Northern Hemisphere at 200 300 hPa. This is higher than the\n5% value reported in R13 and shifted northward, although the\noverall pattern is still comparable. The future aviation impacts decrease only in the SSP1 scenario, while maintaining a similar\n440\nzonal distribution. Aviation-induced BC concentrations are reduced to 0.2–0.5 ng m−3 in 2030 and further down to 0.1 ng m−3\nover most of the northern latitudes in this scenario (Fig. 7e,n), with minor changes in relative terms (Fig. 7f,o). In SSP2, the\nabsolute impact remains approximately constant until 2050 (Fig. 4.3\nAviation This is due to the high efficiency of\nthe combustion processes in aircraft engines, which lead to the formation of smaller BC particles compared to other sectors\n(Petzold et al., 1999). Furthermore, sulphur dioxide in aircraft plumes can drive efficient new particle formation in the upper which is particularly distinct in the upper troposphere due to the low availability of ammonia (e.g. Unger et al., 2013). 450\nDespite the relatively small impact on mass concentrations, of the order of a few nanograms per cubic meter, the aviation\nsector significantly increase the particle number concentration in the upper troposphere. This is due to the high efficiency of\nthe combustion processes in aircraft engines, which lead to the formation of smaller BC particles compared to other sectors\n(Petzold et al., 1999). Furthermore, sulphur dioxide in aircraft plumes can drive efficient new particle formation in the upper troposphere, contributing to particle number concentrations. Aviation-induced particle number concentrations as high as 200–\n455\n500 cm−3 are found in the upper-tropospheric northern mid-latitudes in 2015 (Fig. 8b). This is about one order of magnitude\nlower than in R13. The aviation impact also propagates to lower levels, with induced changes of a few tens of particles per\ncubic centimeter, with potential impacts on low level clouds. The strongest relative contribution of aviation to particle number\nconcentration, however, remains confined in the upper troposphere at mid-latitudes, with values of 20–30%, in line with R13. troposphere, contributing to particle number concentrations. Aviation-induced particle number concentrations as high as 200–\n455\n500 cm−3 are found in the upper-tropospheric northern mid-latitudes in 2015 (Fig. 8b). This is about one order of magnitude\nlower than in R13. The aviation impact also propagates to lower levels, with induced changes of a few tens of particles per\ncubic centimeter, with potential impacts on low level clouds. The strongest relative contribution of aviation to particle number\nconcentration, however, remains confined in the upper troposphere at mid-latitudes, with values of 20–30%, in line with R13. This means that both the aviation impact and the background are lower in this study than in R13. This could be due to the\n460\nimproved representation of the nucleation process in MADE3, which assumes a larger size of 10 nm for newly formed particles\nwith respect to its predecessor version, in order to better reproduce the observations in the upper troposphere (Kaiser et al.,\n2019). 4.3\nAviation As a consequence, the nucleation process leads to the formation of a lower number of particles (see again Fig. 8a). The aviation impact on particle number concentrations remain large in all future scenarios. The impacts in the lower tropo- The aviation impact on particle number concentrations remain large in all future scenarios. The impacts in the lower tropo\nsphere, in particular, increase to values larger than 10 cm−3 in all SSPs, with potential consequences for the aerosol interactions\n465\nwith low clouds. Interestingly, the relative contribution show a counter-intuitive development, with the highest values in SSP1\nand SSP2 (> 60%) and smaller in SSP3. This can be explained with the to relatively long lifetime of the fleet in the aviation\nsector, leading to a slower implementation of technical and policy measures in this sector, which therefore reacts less promptly\nthan the other sectors in determining the background concentrations. In SSP1, for example, background concentrations are very sphere, in particular, increase to values larger than 10 cm−3 in all SSPs, with potential consequences for the aerosol interactions\n465\nwith low clouds. Interestingly, the relative contribution show a counter-intuitive development, with the highest values in SSP1\nand SSP2 (> 60%) and smaller in SSP3. This can be explained with the to relatively long lifetime of the fleet in the aviation\nsector, leading to a slower implementation of technical and policy measures in this sector, which therefore reacts less promptly\nthan the other sectors in determining the background concentrations. In SSP1, for example, background concentrations are very 22 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. efficiently reduced in 2030 and 2050 (Fig. 8d,m), while aviation follows a less efficient reduction path (Fig. 8e,n), resulting\n470\nin a higher relative contribution to overall pollution in the future than in SSP3 (Fig. 8f,o). Note that, according to Feng et al. (2020), the geographical distribution of aviation emissions in the CMIP6 inventory remains the same as in 2015 in all SSPs\nand is simply scaled to match the global emissions. 5\nClimate impacts The climate impact of the transport sectors is depicted in terms of all-sky and clear-sky aerosol RF in Fig. 9. The first considers\n475\nthe changes in radiative fluxes on both cloudy and cloud-free model grid-boxes in each model time-step, whereas the latter is\na diagnostic quantity calculated ignoring the effect of clouds on the radiative fluxes. The comparison of these two quantities\nhelps understanding the role of aerosol-cloud interactions in aerosol-induced radiative forcing. The climate impact of the transport sectors is depicted in terms of all-sky and clear-sky aerosol RF in Fig. 9. The first considers\n475\nthe changes in radiative fluxes on both cloudy and cloud-free model grid-boxes in each model time-step, whereas the latter is\na diagnostic quantity calculated ignoring the effect of clouds on the radiative fluxes. The comparison of these two quantities\nhelps understanding the role of aerosol-cloud interactions in aerosol-induced radiative forcing. Aerosol from the land transport sector accounts for a RF of −164 mW m−2 in 2015 (Fig. 9a), representing the largest\nimpact among the three sectors. This estimate is considerably larger than the value of −12 mW m−2 reported in R13. Although\n480\nboth studies assume the same size distribution for the particles emitted by land transport (based on Birmili et al., 2009), here\nwe further assume that primary sulfate aerosol particle from land transport are emitted in the Aitken mode with a geometric\nmean diameter of 30 nm (see Table 1). This results in a larger land-transport-induced impact on particle number concentration\n(Fig. 4b,c) than in R13, especially in relative terms, with land-transport contributions to particle number well above 20–30% impact among the three sectors. This estimate is considerably larger than the value of −12 mW m−2 reported in R13. Although\n480\nboth studies assume the same size distribution for the particles emitted by land transport (based on Birmili et al., 2009), here\nwe further assume that primary sulfate aerosol particle from land transport are emitted in the Aitken mode with a geometric\nmean diameter of 30 nm (see Table 1). This results in a larger land-transport-induced impact on particle number concentration\n(Fig. 4b,c) than in R13, especially in relative terms, with land-transport contributions to particle number well above 20–30% in several regions of the world. 5\nClimate impacts Aerosol from shipping exerts a RF of −145 mW m−2 in the year 2015 (Fig. 9b), which is slightly smaller than the RF\n495\nof −181 mW m−2 found in R13. This is a consequence of the assumption on the size distribution of ship-emitted particles\nin this study: although we use the same size distribution parameters by Petzold et al. (2008), we assigned only 10% of the\nemitted mass to the Aitken mode rather than 20% as in R13, hence assuming a more aged population. This reduces the\nnumber of emitted particles and in turn the impact of these particles on clouds and the RF. Another reason, as discussed in Aerosol from shipping exerts a RF of −145 mW m−2 in the year 2015 (Fig. 9b), which is slightly smaller than the RF\n495\nof −181 mW m−2 found in R13. This is a consequence of the assumption on the size distribution of ship-emitted particles\nin this study: although we use the same size distribution parameters by Petzold et al. (2008), we assigned only 10% of the\nemitted mass to the Aitken mode rather than 20% as in R13, hence assuming a more aged population. This reduces the\nnumber of emitted particles and in turn the impact of these particles on clouds and the RF. Another reason, as discussed in Aerosol from shipping exerts a RF of −145 mW m−2 in the year 2015 (Fig. 9b), which is slightly smaller than the RF\n495\nof −181 mW m−2 found in R13. This is a consequence of the assumption on the size distribution of ship-emitted particles\nin this study: although we use the same size distribution parameters by Petzold et al. (2008), we assigned only 10% of the\nemitted mass to the Aitken mode rather than 20% as in R13, hence assuming a more aged population. This reduces the\nnumber of emitted particles and in turn the impact of these particles on clouds and the RF. Another reason, as discussed in Sect. 4.2, is the lower shipping-induced aerosol sulfate in this study, following the introduction of the MARPOL regulations\n500\nof FSC starting from 2010. Since sulfate has a remarkable contribution to cloud condensation nuclei, its reduction affects the\nRF impact via the aerosol indirect effect. As for land-transport, the shipping RF is significant at the northern mid-latitudes Sect. 5\nClimate impacts This is also due to a different representation of the aerosol particle background in the present\n485\nstudy, which is generally lower than in R13 (Fig. 4a) and in better agreement with observations (Kaiser et al., 2019). The zonal\nprofiles show that the effect is particularly strong in the northern mid-latitudes (Fig. S19a), where most emissions are released. The RFs simulated in the future scenarios clearly match the narratives underlying the SSPs. In SSP1, the land-transport RF is\nstrongly reduced (in absolute terms) to −28 mW m−2 in 2030 and becomes statistically non-significant in 2050. A less marked in several regions of the world. This is also due to a different representation of the aerosol particle background in the present\n485\nstudy, which is generally lower than in R13 (Fig. 4a) and in better agreement with observations (Kaiser et al., 2019). The zonal\nprofiles show that the effect is particularly strong in the northern mid-latitudes (Fig. S19a), where most emissions are released. The RFs simulated in the future scenarios clearly match the narratives underlying the SSPs. In SSP1, the land-transport RF is\nstrongly reduced (in absolute terms) to −28 mW m−2 in 2030 and becomes statistically non-significant in 2050. A less marked reduction in the climate impact of land transport characterizes SSP2, with a RF of −122 mW m−2 in 2030 and −106 mW m−2\n490\nin 2050. Consistently with the narrative of the SSP3 scenario, an increase in RF is simulated here, with a stronger land-\ntransport-induced cooling effect of −194 mW m−2 in 2030 and −205 mW m−2 in 2050. In all cases, the all-sky RF dominates\nover the clear-sky component, revealing that the land-transport-induced climate impact mostly results from the interactions\nbetween aerosol and clouds, thus confirming the results of R13 and Righi et al. (2015). reduction in the climate impact of land transport characterizes SSP2, with a RF of −122 mW m−2 in 2030 and −106 mW m−2\n490\nin 2050. Consistently with the narrative of the SSP3 scenario, an increase in RF is simulated here, with a stronger land-\ntransport-induced cooling effect of −194 mW m−2 in 2030 and −205 mW m−2 in 2050. In all cases, the all-sky RF dominates\nover the clear-sky component, revealing that the land-transport-induced climate impact mostly results from the interactions\nbetween aerosol and clouds, thus confirming the results of R13 and Righi et al. (2015). 5\nClimate impacts 4.2, is the lower shipping-induced aerosol sulfate in this study, following the introduction of the MARPOL regulations\n500\nof FSC starting from 2010. Since sulfate has a remarkable contribution to cloud condensation nuclei, its reduction affects the\nRF impact via the aerosol indirect effect. As for land-transport, the shipping RF is significant at the northern mid-latitudes 23 2015\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n200\n150\n100\n50\n0\nRF [mW m\n2]\n(a) - Land transport RF\nall-sky\nclear-sky\n2015\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n200\n150\n100\n50\n0\nRF [mW m\n2]\n(b) - Shipping RF\nall-sky\nclear-sky\n2015\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n200\n150\n100\n50\n0\nRF [mW m\n2]\n(c) - Aviation RF\nall-sky\nclear-sky\nFigure 9. All-sky (solid) and clear-sky (hatched) RF of land transport, shipping and aviation in the year 2015 and for the three SSP scenarios\nin 2030 and 2050. The error bars represent the 95% confidence interval and refer to the all-sky RF. The crosses indicate non-significant\nresults. https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 2015\n2030\nSSP1\n2030\nSSP2\n2030\nSSP3\n2050\nSSP1\n2050\nSSP2\n2050\nSSP3\n200\n150\n100\n50\n0\n(c) - Aviation RF\nall-sky\nclear-sky Figure 9. All-sky (solid) and clear-sky (hatched) RF of land transport, shipping and aviation in the year 2015 and for the three SSP scenarios\nin 2030 and 2050. The error bars represent the 95% confidence interval and refer to the all-sky RF. The crosses indicate non-significant\nresults. (Fig. S19b), but has an additional very strong component in the southern hemisphere, where ship traffic is still relevant along\na few routes and, most importantly, aerosol and precursors are released in a pristine background (Fig. 6a), where the cloud a few routes and, most importantly, aerosol and precursors are released in a pristine background (Fig. 6a), where the cloud\nsusceptibility can be high. The shipping-induced negative RF is reduced in all scenarios in 2030, mostly for SSP1 and SSP2\n505\n(from −145 mW m−2 to −99 and −95 mW m−2, respectively), while in SSP3 the reduction is marginal (to −131 mW m−2). 5\nClimate impacts As discussed above, this is due to the larger relative impacts of aviation on particle number concen-\ntration in the current study, following an improved representation of background particles, in particular the ones generated by contrary, projects a non-significant aerosol climate effect from shipping in this year, consistent with the large reductions in\n510\nshipping SO2 emissions in this scenario (see Sect. 3 and Fig. 1d) and still relevant background sulfate pollution (Fig. 5p). The impact of aviation-induced aerosol in 2015 is quantified as −64 mW m−2, a factor of about four larger than the estimate\nin R13 (−15 mW m−2). As discussed above, this is due to the larger relative impacts of aviation on particle number concen-\ntration in the current study, following an improved representation of background particles, in particular the ones generated by the new particle formation process (Kaiser et al., 2019). Although other modelling groups have confirmed these large aviation\n515\nRF (Gettelman and Chen, 2013; Kapadia et al., 2016), the effect of aviation-induced aerosol on liquid clouds is still considered\nquite speculative (Lee et al., 2021), as large uncertainties exist in the size distribution of emitted particles and in the effective-\nness of the downward transport of aviation-induced particles from typical flight altitudes (8–12 km) to the lower atmospheric\nlayers, where they could potentially impact liquid phase clouds by acting as condensation nuclei. All SSPs project a steadily increasing impact of the aviation RF, with the strongest growth in SSP2, reaching a RF of −126 mW m−2 in 2050. As argued\n520\nin Righi et al. (2016), the long lifetime of the aviation fleet and the tight constraints on safety and costs make the introduction\nof mitigation measures in aviation more challenging than in other sectors. In all scenarios the mitigation measures do not lead\nto a reduction of the overall aviation emissions, which are instead only modulated by different growth rates in overall traffic\nvolumes. increasing impact of the aviation RF, with the strongest growth in SSP2, reaching a RF of −126 mW m−2 in 2050. As argued\n520\nin Righi et al. (2016), the long lifetime of the aviation fleet and the tight constraints on safety and costs make the introduction\nof mitigation measures in aviation more challenging than in other sectors. 5\nClimate impacts In\n2050, the greenest SSP1 scenario has the largest climate impact (−120 mW m−2), comparable to SSP3 (−110 mW m−2). This\nmight appear counterintuitive, but can be explained with the much lower particle number background in this clean scenario\n(Fig. 6d), such that even a relatively small amount of shipping-induced particles leads to a large RF effect. SSP2, on the\ncontrary, projects a non-significant aerosol climate effect from shipping in this year, consistent with the large reductions in\n510 susceptibility can be high. The shipping-induced negative RF is reduced in all scenarios in 2030, mostly for SSP1 and SSP2\n505\n(from −145 mW m−2 to −99 and −95 mW m−2, respectively), while in SSP3 the reduction is marginal (to −131 mW m−2). In\n2050, the greenest SSP1 scenario has the largest climate impact (−120 mW m−2), comparable to SSP3 (−110 mW m−2). This\nmight appear counterintuitive, but can be explained with the much lower particle number background in this clean scenario\n(Fig. 6d), such that even a relatively small amount of shipping-induced particles leads to a large RF effect. SSP2, on the contrary, projects a non-significant aerosol climate effect from shipping in this year, consistent with the large reductions in\n510\nshipping SO2 emissions in this scenario (see Sect. 3 and Fig. 1d) and still relevant background sulfate pollution (Fig. 5p). The impact of aviation-induced aerosol in 2015 is quantified as −64 mW m−2, a factor of about four larger than the estimate\nin R13 (−15 mW m−2). As discussed above, this is due to the larger relative impacts of aviation on particle number concen-\ntration in the current study, following an improved representation of background particles, in particular the ones generated by contrary, projects a non-significant aerosol climate effect from shipping in this year, consistent with the large reductions in\n510\nshipping SO2 emissions in this scenario (see Sect. 3 and Fig. 1d) and still relevant background sulfate pollution (Fig. 5p). The impact of aviation-induced aerosol in 2015 is quantified as −64 mW m−2, a factor of about four larger than the estimate\nin R13 (−15 mW m−2). 5\nClimate impacts In all scenarios the mitigation measures do not lead\nto a reduction of the overall aviation emissions, which are instead only modulated by different growth rates in overall traffic\nvolumes. 24 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 6\nConclusions and outlook\n525 The results for the transport-induced aerosol mass concentrations generally confirm the findings of R13, although\nsome major differences exist, especially in the geographical distribution of the transport impacts. These are essentially due to the updated emission inventory used as an input to the model simulations and the different year under investigation (2015 vs. 540\n2000), but also to the improvement of the model. Substantial differences, on the other hand, are found in terms of simulated\ntransport-induced impacts on aerosol number concentration. This is because the new MADE3 model version adopted here is\ncharacterized by a generally lower background concentration of particles, as a result of different assumptions on the particles\nsize distribution upon emissions by anthropogenic sources (including transport) and an improved representation of the aging of the updated emission inventory used as an input to the model simulations and the different year under investigation (2015 vs. 540\n2000), but also to the improvement of the model. Substantial differences, on the other hand, are found in terms of simulated\ntransport-induced impacts on aerosol number concentration. This is because the new MADE3 model version adopted here is\ncharacterized by a generally lower background concentration of particles, as a result of different assumptions on the particles\nsize distribution upon emissions by anthropogenic sources (including transport) and an improved representation of the aging of the updated emission inventory used as an input to the model simulations and the different year under investigation (2015 vs. 540\n2000), but also to the improvement of the model. Substantial differences, on the other hand, are found in terms of simulated\ntransport-induced impacts on aerosol number concentration. This is because the new MADE3 model version adopted here is\ncharacterized by a generally lower background concentration of particles, as a result of different assumptions on the particles\nsize distribution upon emissions by anthropogenic sources (including transport) and an improved representation of the aging of newly-formed particles via the nucleation process. The extensive evaluation performed by Kaiser et al. (2019) and Righi et al. 545\n(2020) shows that the new model version provides a better representation of the aerosol background and the climate mean-state\nof the key cloud and radiation variables compared with observations. 6\nConclusions and outlook\n525 In this study, the global impact of the transport sectors (land transport, shipping and aviation) on aerosol and the resulting\nclimate effects for present-day (2015) and three future projections for 2030 and 2050 have been assessed using the most recent\nCMIP6 emission inventory and the SSPs scenarios as an input to a state-of-the-art global aerosol-climate model, including\ndetailed aerosol microphysics and an explicit representation of aerosol-radiation and aerosol-cloud interactions. The results have been compared with the findings of our previous assessments (Righi et al., 2013, 2015, 2016), which used a predecessor\n530\nversion of the same model and the earlier CMIP5 emissions data for the year 2000 combined with the RCP projections for\n2030. Black carbon and nitrate are found to be highly relevant pollutants from land transport, which contributes 15–20% to the near-\nsurface concentrations of these species. The shipping sector is also an important source of nitrate pollution, responsible for more have been compared with the findings of our previous assessments (Righi et al., 2013, 2015, 2016), which used a predecessor\n530\nversion of the same model and the earlier CMIP5 emissions data for the year 2000 combined with the RCP projections for\n2030. Black carbon and nitrate are found to be highly relevant pollutants from land transport, which contributes 15–20% to the near-\nsurface concentrations of these species. The shipping sector is also an important source of nitrate pollution, responsible for more than 20% of its near-surface concentration over the oceans, and still contributes significantly to aerosol sulfate pollution, despite\n535\nthe introduction of control measures for fuel sulphur content in shipping by MARPOL since 2010. The aviation contribution to\nthe aerosol mass concentrations in the upper-troposphere is generally small (below 5%), while it is larger for particle number\n(5–10%). The results for the transport-induced aerosol mass concentrations generally confirm the findings of R13, although\nsome major differences exist, especially in the geographical distribution of the transport impacts. These are essentially due to than 20% of its near-surface concentration over the oceans, and still contributes significantly to aerosol sulfate pollution, despite\n535\nthe introduction of control measures for fuel sulphur content in shipping by MARPOL since 2010. The aviation contribution to\nthe aerosol mass concentrations in the upper-troposphere is generally small (below 5%), while it is larger for particle number\n(5–10%). 6\nConclusions and outlook\n525 This results in larger RF effects for land transport and\naviation with respect to the previous assessment, while a smaller RF is simulated for shipping, thanks also to the reduced sulfate\nimpacts following the MARPOL regulations on fuel sulphur content. newly-formed particles via the nucleation process. The extensive evaluation performed by Kaiser et al. (2019) and Righi et al. 545\n(2020) shows that the new model version provides a better representation of the aerosol background and the climate mean-state\nof the key cloud and radiation variables compared with observations. This results in larger RF effects for land transport and\naviation with respect to the previous assessment, while a smaller RF is simulated for shipping, thanks also to the reduced sulfate\nimpacts following the MARPOL regulations on fuel sulphur content. The mid- and long-term projections of the transport effects under the SSPs are generally consistent with the narratives\n550\nunderlying the scenarios. SSP1 is characterized by the lowest impacts, with a strong decrease of the land transport effects,\na slight decrease for shipping and a lower increase than the other scenarios for aviation. The climate impact of shipping and\naviation in this scenario are still relatively high despite a strong reduction in their emissions, which can be ascribed to the very\nlow background concentrations, which is mostly controlled by the emissions of the other anthropogenic sectors. The results for The mid- and long-term projections of the transport effects under the SSPs are generally consistent with the narratives\n550\nunderlying the scenarios. SSP1 is characterized by the lowest impacts, with a strong decrease of the land transport effects,\na slight decrease for shipping and a lower increase than the other scenarios for aviation. The climate impact of shipping and\naviation in this scenario are still relatively high despite a strong reduction in their emissions, which can be ascribed to the very\nlow background concentrations, which is mostly controlled by the emissions of the other anthropogenic sectors. The results for the SSP2 scenario confirm its middle-of-the-road nature, which is evident especially in the impacts of the land transport sector. 555\nA peculiarity of this scenario it the low climate impact of shipping, due to low emissions from this sector combined with still\nelevated aerosols levels induced by other sectors, and the relatively large impact of aviation, whose emissions in this scenario\nare still high compared to other sectors. 6\nConclusions and outlook\n525 their consequence on the atmospheric composition, for example hydrogen (Popa et al., 2015), and new particle types resulting\n580\nfrom non-combustion processes (Grigoratos and Martini, 2014; Timmers and Achten, 2016), whose impact is projected to\nbecome increasingly important with the progressive electrification of the land transport sector. their consequence on the atmospheric composition, for example hydrogen (Popa et al., 2015), and new particle types resulting\n580\nfrom non-combustion processes (Grigoratos and Martini, 2014; Timmers and Achten, 2016), whose impact is projected to\nbecome increasingly important with the progressive electrification of the land transport sector. Code and data availability. MESSy is continuously developed and applied by a consortium of institutions. The usage of MESSy and access\nto the source code is licensed to all affiliates of institutions which are members of the MESSy Consortium. Institutions can become members Code and data availability. MESSy is continuously developed and applied by a consortium of institutions. The usage of MESSy and access\nto the source code is licensed to all affiliates of institutions which are members of the MESSy Consortium. Institutions can become members Code and data availability. MESSy is continuously developed and applied by a consortium of institutions. The usage of MESSy and access\nto the source code is licensed to all affiliates of institutions which are members of the MESSy Consortium. Institutions can become members\nof the MESSy Consortium by signing the MESSy Memorandum of Understanding. More information can be found on the MESSy Consor-\n585\ntium Website (http://www.messy-interface.org, last access: 22 November 2022). The model configuration discussed in this paper is based on\nEMAC version 2.55. The exact setup used to produce the result of this paper is archived at the German Climate Computing Center (DKRZ)\nand can be made available to members of the MESSy community upon request. The output of the model simulations discussed in this work\nwill be made available via DOI in the final version of this paper. of the MESSy Consortium by signing the MESSy Memorandum of Understanding. More information can be found on the MESSy Consor-\n585\ntium Website (http://www.messy-interface.org, last access: 22 November 2022). The model configuration discussed in this paper is based on\nEMAC version 2.55. The exact setup used to produce the result of this paper is archived at the German Climate Computing Center (DKRZ)\nand can be made available to members of the MESSy community upon request. 6\nConclusions and outlook\n525 The prominent role of the background has further consequences for\nthe development and application of climate response models (e.g., Grewe and Stenke, 2008; Wild et al., 2012; Dahlmann et al.,\n2016; Rieger and Grewe, 2022) to assess the transportation impacts of aerosol, implying that the global model simulations for training these models shall be performed of varying background conditions on the region of interest. 575\nOn the policy side, a further challenge is posed by the trade-offs connected with the implementation of specific measures. One\nprominent example is the reduction in fuel sulfur content in shipping to improve air quality in the coastlines, that at the same\ntime leads to a reduction of the ship-induced cooling via the aerosol indirect-effect, in turn reducing the offset of greenhouse\nwarning (Quaas et al., 2022). Future modelling studies will also need to deal with the growing impacts of alternative fuels and training these models shall be performed of varying background conditions on the region of interest. 575\nOn the policy side, a further challenge is posed by the trade-offs connected with the implementation of specific measures. One\nprominent example is the reduction in fuel sulfur content in shipping to improve air quality in the coastlines, that at the same\ntime leads to a reduction of the ship-induced cooling via the aerosol indirect-effect, in turn reducing the offset of greenhouse\nwarning (Quaas et al., 2022). Future modelling studies will also need to deal with the growing impacts of alternative fuels and training these models shall be performed of varying background conditions on the region of interest. 575\nOn the policy side, a further challenge is posed by the trade-offs connected with the implementation of specific measures. One\nprominent example is the reduction in fuel sulfur content in shipping to improve air quality in the coastlines, that at the same\ntime leads to a reduction of the ship-induced cooling via the aerosol indirect-effect, in turn reducing the offset of greenhouse\nwarning (Quaas et al., 2022). Future modelling studies will also need to deal with the growing impacts of alternative fuels and\ntheir consequence on the atmospheric composition, for example hydrogen (Popa et al., 2015), and new particle types resulting\n580\nfrom non-combustion processes (Grigoratos and Martini, 2014; Timmers and Achten, 2016), whose impact is projected to\nbecome increasingly important with the progressive electrification of the land transport sector. 6\nConclusions and outlook\n525 The representation of the background is also a modelling challenge and our results the other hand, the representation of the aerosol concentration background in the model can play a significant role in charac-\n565\nterizing the impact of the perturbation introduced by the emissions of the specific sectors. This is particularly the case when\nanalysing the scenario projections, as the potential reductions in the emissions for a given sector needs to be set in the context of\nthe emission changes in other sectors. Policy measures cannot therefore be evaluated only focusing on a single sector, but have\nto be considered as part of a larger picture. The representation of the background is also a modelling challenge and our results suggest that an accurate representation of the aerosol background concentrations is important and can significantly affect the\n570\nestimated impacts, as it is clear when comparing the outcome of the present study with the result of the previous assessment\nobtained with a predecessor version of the same model. The prominent role of the background has further consequences for\nthe development and application of climate response models (e.g., Grewe and Stenke, 2008; Wild et al., 2012; Dahlmann et al.,\n2016; Rieger and Grewe, 2022) to assess the transportation impacts of aerosol, implying that the global model simulations for suggest that an accurate representation of the aerosol background concentrations is important and can significantly affect the\n570\nestimated impacts, as it is clear when comparing the outcome of the present study with the result of the previous assessment\nobtained with a predecessor version of the same model. The prominent role of the background has further consequences for\nthe development and application of climate response models (e.g., Grewe and Stenke, 2008; Wild et al., 2012; Dahlmann et al.,\n2016; Rieger and Grewe, 2022) to assess the transportation impacts of aerosol, implying that the global model simulations for suggest that an accurate representation of the aerosol background concentrations is important and can significantly affect the\n570\nestimated impacts, as it is clear when comparing the outcome of the present study with the result of the previous assessment\nobtained with a predecessor version of the same model. 6\nConclusions and outlook\n525 The strong growth of pollution characterizing SSP3 is remarkable, showing steadily the SSP2 scenario confirm its middle-of-the-road nature, which is evident especially in the impacts of the land transport sector. 555\nA peculiarity of this scenario it the low climate impact of shipping, due to low emissions from this sector combined with still\nelevated aerosols levels induced by other sectors, and the relatively large impact of aviation, whose emissions in this scenario\nare still high compared to other sectors. The strong growth of pollution characterizing SSP3 is remarkable, showing steadily the SSP2 scenario confirm its middle-of-the-road nature, which is evident especially in the impacts of the land transport sector. 555\nA peculiarity of this scenario it the low climate impact of shipping, due to low emissions from this sector combined with still\nelevated aerosols levels induced by other sectors, and the relatively large impact of aviation, whose emissions in this scenario\nare still high compared to other sectors. The strong growth of pollution characterizing SSP3 is remarkable, showing steadily 25 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. growing impacts in 2030 and 2050 with respect to 2015, especially in the land transport sector. These results show that the\nimpact of the transport sectors under the SSPs are more consistent with the respective storylines than in the predecessor RCP\n560\nscenarios used in (Righi et al., 2015, 2016). From the analysis of the simulations in this study we can conclude that the challenges in quantifying the impact of the trans-\nportation sectors on aerosol and climate are twofold. On the one hand, reliable emission inventories are essential for a correct\nquantification of the resulting impacts: this concerns not only the total emitted mass, but also its geographical distribution. On the other hand, the representation of the aerosol concentration background in the model can play a significant role in charac-\n565\nterizing the impact of the perturbation introduced by the emissions of the specific sectors. This is particularly the case when\nanalysing the scenario projections, as the potential reductions in the emissions for a given sector needs to be set in the context of\nthe emission changes in other sectors. Policy measures cannot therefore be evaluated only focusing on a single sector, but have\nto be considered as part of a larger picture. Financial support. This study was supported by the DLR transport research program (TraK and DATAMOST projects).\n600 6\nConclusions and outlook\n525 The output of the model simulations discussed in this work\nwill be made available via DOI in the final version of this paper. 26 https://doi.org/10.5194/esd-2022-52\nPreprint. Discussion started: 4 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Author contributions. MR conceived the study, designed and performed the simulations, analysed the data and wrote the paper. JH conceived\n590\nthe study, contributed to the simulation design and to the text. SB prepared the CMIP6/SSP emission data used to drive the model simulations\nand contributed to the text. Competing interests. The authors declare no competing interests. Competing interests. The authors declare no competing interests. Competing interests. The authors declare no competing interests. Acknowledgements. We are grateful to Axel Lauer (DLR) for his valuable comments on an earlier version of this manuscript, and to Christof\nBeer, Patrick Jöckel and Mariano Mertens (DLR) for helpful suggestions. The processing of the CMIP6/SSP emissions for usage in EMAC\n595\nhas greatly benefited from the efforts by Markus Kunze (Freie Universität Berlin) and Phoebe Graf (DLR). MR would also like to thank\nManuel Schlund (DLR), for his competent help with the matplotlib Python library. The model simulations and data analysis for this work\nused the resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID\nbd0080. Beer, Patrick Jöckel and Mariano Mertens (DLR) for helpful suggestions. The processing of the CMIP6/SSP emissions for usage in EMAC\n595\nhas greatly benefited from the efforts by Markus Kunze (Freie Universität Berlin) and Phoebe Graf (DLR). MR would also like to thank\nManuel Schlund (DLR), for his competent help with the matplotlib Python library. The model simulations and data analysis for this work\nused the resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID\nbd0080. Beer, Patrick Jöckel and Mariano Mertens (DLR) for helpful suggestions. The processing of the CMIP6/SSP emissions for usage in EMAC\n595\nhas greatly benefited from the efforts by Markus Kunze (Freie Universität Berlin) and Phoebe Graf (DLR). MR would also like to thank\nManuel Schlund (DLR), for his competent help with the matplotlib Python library. 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