# Historical and future changes in air pollutants from CMIP6 models

Steven Turnock, Robert Allen, Martin Andrews, Susanne Bauer, Makoto Deushi, Louisa Emmons, Peter Good, Larry Horowitz, Jasmin John, Martine Michou, et al.

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Steven Turnock, Robert Allen, Martin Andrews, Susanne Bauer, Makoto Deushi, et al. Historical and future changes in air pollutants from CMIP6 models. Atmospheric Chemistry and Physics, 2020, 20 (23), pp.14547-14579. 10.5194/acp-20-14547-2020. hal-03975381

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# Historical and future changes in air pollutants from CMIP6 models

Steven T. Turnock<fnref n="1" />, Robert J. Allen<fnref n="2" />, Martin Andrews<fnref n="1" />, Susanne E. Bauer<fnref n="3" />,<fnref n="4" />, Makoto Deushi<fnref n="5" />, Louisa Emmons<fnref n="6" />, Peter Good<fnref n="1" />, Larry Horowitz<fnref n="7" />, Jasmin G. John<fnref n="7" />, Martine Michou<fnref n="8" />, Pierre Nabat<fnref n="8" />, Vaishali Naik<fnref n="7" />, David Neubauer<fnref n="9" />, Fiona M. O’Connor<fnref n="1" />, Dirk Olivié<fnref n="10" />, Naga Oshima<fnref n="5" />, Michael Schulz<fnref n="10" />, Alistair Sellar<fnref n="1" />, Sungbo Shim<fnref n="11" />, Toshihiko Takemura<fnref n="12" />, Simone Tilmes<fnref n="6" />, Kostas Tsigaridis<fnref n="3" />,<fnref n="4" />, Tongwen Wu<fnref n="13" />, and Jie Zhang<fnref n="13" />

**Correspondence:** Steven T. Turnock (steven.turnock@metoffice.gov.uk)

Received: 30 December 2019 – Discussion started: 21 January 2020

Revised: 1 October 2020 – Accepted: 9 October 2020 – Published: 30 November 2020

**Abstract.** Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone ($O_3$) and particulate matter less than 2.5 µm in diameter ($PM_{2.5}$) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of $O_3$ and $PM_{2.5}$. CMIP6 models consistently overestimate observed surface $O_3$ concentrations

across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface $PM_{2.5}$ concentrations are consistently underestimated in CMIP6 models by up to 10 µg m⁻<fnref n="3" />, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of $O_3$ and $PM_{2.5}$ are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface $O_3$ and $PM_{2.5}$ are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for $O_3$ and 12 µg m⁻<fnref n="3" /> for $PM_{2.5}$. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for $O_3$ and 12 µg m⁻<fnref n="3" /> for $PM_{2.5}$. However, for sce-

<footnote n="1">¹Met Office Hadley Centre, Exeter, UK</footnote>

<footnote n="2">²Department of Earth and Planetary Sciences, University of California Riverside, Riverside, CA, USA</footnote>

<footnote n="3">³Center for Climate Systems Research, Columbia University, New York, NY, USA</footnote>

<footnote n="4">⁴NASA Goddard Institute for Space Studies, New York, NY, USA</footnote>

<footnote n="5">⁵Meteorological Research Institute, Tsukuba, Japan</footnote>

<footnote n="6">⁶Atmospheric Chemistry Observations and Modelling Lab, National Center for Atmospheric Research, Boulder, CO, USA</footnote>

<footnote n="7">⁷NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA</footnote>

<footnote n="8">⁸Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France</footnote>

<footnote n="9">⁹Institute of Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland</footnote>

<footnote n="10">¹⁰Division for Climate Modelling and Air Pollution, Norwegian Meteorological Institute, Oslo, Norway</footnote>

<footnote n="11">¹¹National Institute of Meteorological Sciences, Seogwipo-si, Jeju-do, Korea</footnote>

<footnote n="12">¹²Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan</footnote>

<footnote n="13">¹³Beijing Climate Center, China Meteorological Administration, Beijing, China</footnote>
narios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O₃ (up 10 ppb) and PM₂.₅ (up to 8 µg m⁻³) are simulated across most regions, although, for regions like North America and Europe small reductions in PM₂.₅ are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O₃ and PM₂.₅ from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

*Copyright statement.* The works published in this journal are distributed under the Creative Commons Attribution 4.0 License. This license does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 4.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other. The co-authors Steven T. Turnock, Martin Andrews, Peter Good, Fiona M. O'Connor and Alistair Sellar are employees of the UK Government and therefore claim Crown copyright for the respective contributions.

© Crown copyright 2020

# 1 Introduction

Air pollutants are important atmospheric constituents as they have large impacts on human health (Lelieveld et al., 2015), damage ecosystems (Fowler et al., 2009) and can also influence climate through changes in the Earth's radiative balance (Boucher et al., 2013; Myhre et al., 2013). Two major components of air pollution at the surface are ozone (O₃) and particulate matter less than 2.5 µm in diameter (PM₂.₅). Exposure to present-day ambient concentrations of these two air pollutants was estimated to cause up to 4 million premature deaths per year (Apte et al., 2015; Malley et al., 2017). Over recent decades, the impact on human health from exposure to air pollutants has been increasing (Butt et al., 2017; Cohen et al., 2017). Additionally, elevated levels of air pollutants over recent decades have also been responsible for ecosystem damage to crops and vegetation, although there have been recent improvements in environmental health (de Wit et al., 2015).

In terms of climate impact, tropospheric O₃ has a positive radiative forcing on climate over the industrial period and is the third-most important greenhouse gas in terms of radiative forcing (Myhre et al., 2013). However, depletion of O₃ in the stratosphere has resulted in a net negative top-of-atmosphere radiative forcing over recent decades (Checa-Garcia et al., 2018). Particulate matter (PM), also referred to as aerosols, has an overall negative radiative forcing on climate, both directly and indirectly, through the modification of cloud properties (Boucher et al., 2013). Both O₃ and PM are relatively short lived in the troposphere, with a typical lifetime of less than 2 weeks in the lower atmosphere, and are commonly referred to as short-lived climate forcers (SLCFs). Future air pollutant concentrations and distributions are driven by changes to both precursor emissions and climate. Emission control measures on both a national and international level can influence future changes to air pollutants, with global increases in CH₄ abundance potentially offsetting benefits to surface O₃ from local emission reductions (Fiore et al., 2002; Shindell et al., 2012; Wild et al., 2012). For PM₂.₅, changes in concentrations are dependent on both emission rates and levels of atmospheric oxidants, although changes in specific aerosol components can be more directly related to emissions, e.g. black carbon. In a warming world, background O₃ concentrations over remote locations are likely to decrease (Johnson et al., 1999; Isaksen et al., 2009; Fiore et al., 2012; Doherty et al., 2013), whereas over anthropogenic source regions, which have higher average surface O₃ concentrations, an increase is anticipated (Rasmussen et al., 2013; Colette et al., 2015). The climate impact on PM₂.₅ is much more uncertain and variable across regions, with both increases and decreases predicted due to the uncertainty of future meteorological effects (Jacob and Winner, 2009; Allen et al., 2016; Shen et al., 2017). However, any such climate change impacts on PM₂.₅ are considered to be smaller than the effect from implementing emission mitigation measures (Westervelt et al., 2016).

Experiments conducted as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012) and the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP; Lamarque et al., 2013) contributed to a multi-model assessment of future trends in air pollutants. Global annual mean surface O₃ concentrations were predicted to increase by up to 5 ppb in 2100 using RCP8.5 (Representative Concentration Pathway with an anthropogenic radiative forcing of 8.5 W m⁻² in 2100), the RCP with the largest increases in methane (CH₄) abundances and the largest climate change signal used in CMIP5 (Kirtman et al., 2013). The other RCPs used in CMIP5 had a lower climate forcing and smaller changes in CH₄ abundance, with models predicting global annual mean surface O₃ concentrations that showed little change in the short term (up to 2050) but decreased by around 5 ppb in 2100. The scenario differences in the global mean response for surface O₃ were generally reflected across other regions, al-
though with a larger magnitude of change over the North-
ern Hemisphere continental regions. The predicted range of
future surface O3 concentrations was previously found to be
dominated by changes in precursor emissions (Fiore et al.,
2012). However, in regions remote from pollution sources
(low-NOx), future climate change was shown to result in a
small reduction in surface O3 concentrations. For PM2.5, re-
sults from the CMIP5 and ACCMIP models showed annual
mean concentrations declining in most regions and across all
scenarios due to the reduction in aerosol emissions. Glob-
ally, PM2.5 concentrations reduced by ~ 1 µg m-3 by 2100,
whereas larger regional reductions of up to 6 µg m-3 were
predicted by 2100. Exceptions to this occurred over South
and East Asia, where PM2.5 concentrations increased by up
to 3 µg m-3 in the near-term (up to 2050), after which con-
centrations reduced by 2100. The largest difference in the re-
sponse of PM2.5 across the scenarios was also shown across
East and South Asia due to differences in the carbonaceous
and sulphur dioxide (SO2) emission trajectories (Fiore et al.,
2012). Future PM2.5 concentrations over Africa and the Mid-
dle East were shown to be quite noisy due to the large meteo-
rological variability that influences dust emissions over these
regions.

The current set of experiments conducted for the Coupled
Model Intercomparison Project Phase 6 (CMIP6; Eyring et
al., 2016) represent an opportunity to update the assessment
of current and future levels of air pollutants using the latest
generation of Earth system and climate models. A new set of
future scenarios have been generated for CMIP6: the shared
socio-economic pathways (SSPs), which combine different
trends in social, economic and environmental developments
(O'Neill et al., 2014). Varying amounts of emission mitiga-
tion to SLCFs are applied on top of the baseline social and
economic developments to meet predefined climate and air
quality targets in the future, allowing for a wider range of
future air pollutant trajectories to be assessed than what oc-
curred in CMIP5 (Rao et al., 2017; Riahi et al., 2017). Ini-
tial assessments have been made of future changes to air
pollutants in the SSPs using simplified models (Reis et al.,
2018; Turnock et al., 2018, 2019). The sustainability path-
way (SSP1) leads to improvements in both air quality and
climate, whereas SSP3 (regional rivalry) is not compatible
with achieving air quality and climate goals, and the con-
ventional fuels (SSP5) pathway improves air quality at the
expense of climate (Reis et al., 2018). Strong climate and air
pollutant mitigation measures in SSP1 were shown to reduce
global annual mean surface O3 concentrations by more than
3.5 ppb, whereas for SSP3 O3 concentrations over Asia were
predicted to increase by 6 ppb (Turnock et al., 2019). These
studies highlighted the potential large regional variability in
the response of air pollutants to the different assumptions in
the future pathways and also the need for a full model as-
sessment using the current generation of Earth system mod-
els (ESMs) that take into account both changes in emissions
and climate.

In this study, we use results from experiments conducted
as part of CMIP6 to make a first assessment of historical and
future changes in air pollutants. First, we assess the perfor-
mance of CMIP6 models in simulating present-day air pollu-
tants by conducting an evaluation against observations of O3
and PM2.5. Regional changes in surface O3 and PM2.5 are
computed over the historical period (1850–2014) to provide
context with future changes. We are then able to show fu-
ture projections of air pollutants over different world regions
under different shared socio-economic pathways used in the
CMIP6 experiments. Finally, a comparison is made of indi-
vidual CMIP6 models for a single future scenario (ssp370)
to identify potential reasons for model discrepancies.

## 2 Methods

2.1 Air pollutant emissions

A new set of historical and future anthropogenic air pollutant
emissions have been developed and used as part of CMIP6.
The historical anthropogenic emissions are from the Com-
munity Emissions Data System (CEDS), and a new dataset
was developed for biomass-burning emissions, both of which
provide information on emissions from 1750 to 2014 (van
Marle et al., 2017; Hoesly et al., 2018). The SSPs used in fu-
ture CMIP6 experiments represent an update from the RCPs
used in CMIP5 as they combine pathways of socio-economic
development with targets to achieve a certain level of climate
mitigation (O'Neill et al., 2014; van Vuuren et al., 2014; Ri-
ahi et al., 2017). The SSPs are divided into the following five
different pathways depending on their social, economic and
environmental development: SSP1 – sustainability, SSP2 –
middle of the road, SSP3 – regional rivalry, SSP4 – inequal-
ity, SSP5 – fossil fuel development. An assumption about the
degree of air pollution control (strong, medium or weak) is
included on top of the baseline pathway, with stricter air pol-
lution controls assumed to be tied to economic development
(Rao et al., 2016). Weak air pollution controls occur in SSP3
and SSP4, with medium controls in SSP2 and strong air pol-
lution controls in SSP1 and SSP5 (Gidden et al., 2019). A
particular climate mitigation target, in terms of an anthro-
pogenic radiative forcing by 2100, and the range of emis-
sion mitigation measures associated with achieving it are in-
cluded in addition to the existing policy measures within each
baseline SSP scenario. Climate mitigation targets vary from
a weak-mitigation scenario with an anthropogenic radiative
forcing of 8.5 W m⁻² by 2100, comparable with a 5 °C tem-
perature change (Riahi et al., 2017), to a strong-mitigation
scenario with a radiative forcing of 1.9 W m⁻² by 2100, in
accordance with the Paris Agreement for keeping tempera-
tures below 2 °C (United Nations, 2016). Some climate miti-
gation targets are comparable with those of the RCPs used
in CMIP5 (2.6, 4.5 and 6.0), whilst others are new; e.g.
ssp534-over is included as a delayed mitigation scenario. A
scenario specific to the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP), ssp370-lowNTCF, is also included to study the impact of mitigation measures to specifically control SLCFs on top of ssp370. Future biomass-burning emissions vary in each scenario, depending on the particular land-use assumptions (Rao et al., 2017). Whilst future anthropogenic and biomass-burning emissions are prescribed in each CMIP6 model from the same dataset, other natural emissions, e.g. dust, biogenic volatile organic compounds (BVOCs) etc., will be different and depend on the individual model configuration.

Figure 1 shows the future changes in global total (anthropogenic and biomass) emissions of the major air pollutant precursors across all of the CMIP6 scenarios, provided as input to the CMIP6 models. The underlying feature is that global air pollutant emissions are predicted to reduce across the majority of scenarios by 2100. The exception to this is that global and regional emissions increase or remain at present-day levels for ssp370 (Figs. 1 and 2). Some air pollutant emissions increase in the near term in other scenarios, e.g. nitrogen oxides ($NO_x$) in ssp585 (by up to 15%), but by 2100 these have been reduced. Future $CH_4$ abundances show the largest diversity amongst the SSPs. Large increases in global $CH_4$ abundances of more than 50% are predicted for the fossil-fuel-dominated pathways of ssp370 and ssp585, whereas large reductions of ~50% are predicted to occur in the strong-mitigation scenarios of SSP1.

For $SO_2$ large reductions of more than 50% are shown for most scenarios and across most regions (Fig. 2), apart from Africa and Asia in ssp370. Near-term (2050) increases in $SO_2$ occur over South Asia and other developing regions, which are then reduced in the latter half of the 21st century. Over Europe and North America, consistent decreases are predicted across all scenarios. The other major aerosol emissions, OC and BC, show similar reductions to $SO_2$ across all scenarios and regions. For all aerosol and aerosol precursors, a reduction of 80%-100% (relative to 2015) in regional emissions is predicted by 2100 in the strong-mitigation scenarios. Changes in the emissions of the $O_3$ precursors – $NO_x$, CO and non-methane volatile organic compounds (NMVOCs) – show a similar increase across most regions for ssp370 but a general decrease in other scenarios. The change in these emissions is particularly diverse across all the scenarios in South Asia, with large relative increases in ssp370 (of up to 50%) in contrast to the large decreases in ssp126 (up to 40%). Across East Asia there is a 20% increase in $NO_x$ emissions for ssp370 in 2050 but a long-term reduction across all scenarios.

## 2.2 CMIP6 simulations

Surface concentrations of $O_3$ and $PM_{2.5}$ have been obtained from all the CMIP6 models that made appropriate data available to the Earth System Grid Federation (ESGF) at the time of writing. To study changes in surface air pollutants over the

industrial period, data have been obtained from the coupled historical simulations (Eyring et al., 2016) over the period 1850 to 2014 from all of the available ensemble members of each available CMIP6 model. For each model, a mean is taken using all available ensemble members prior to the calculation of the multi-model mean. For model evaluation purposes, 10 years of data from historical simulations have been used over the period that is relevant to the particular observational dataset (2000–2010 for ground-based $PM_{2.5}$, 2004–2014 for $PM_{2.5}$ reanalysis product and 2005–2014 for ground-based $O_3$). To investigate future changes in air pollutants, all available data have been obtained over the period 2015 to 2100 for each of the different future coupled atmosphere-ocean model experiments, conducted as part of ScenarioMIP (O’Neill et al., 2016). CMIP6 model data have also been obtained for the AerChemMIP specific ssp370-lowNTCF scenario, which was only required to be conducted over the period 2015–2055 (Collins et al., 2017).

Concentrations of both pollutants at the surface have been obtained by extracting the lowest vertical level of the full 3D field output on the horizontal and vertical grid of each model (the “AERmon” CMIP6 table ID). For $O_3$, this is supplied as a separate diagnostic which can be used directly. However, models contributing to CMIP6 will not all directly output $PM_{2.5}$, and the calculation of $PM_{2.5}$ will not be consistent across individual models due to the different treatment of aerosols and their components. For example only a few CMIP6 models include the simulation of ammonium nitrate in their aerosol scheme (currently, only GISS-E2-1-G and GFDL-ESM4 have provided nitrate mass mixing ratios on the ESGF database). Therefore, to use a consistent definition across all models, we calculated $PM_{2.5}$ offline. In this study, surface $PM_{2.5}$ is defined as the sum of the individual dry aerosol mass mixing ratios of black carbon (BC), total organic aerosol (OA – both primary and secondary sources), sulphate ($SO_4$), sea salt (SS) and dust (DU) from the lowest model level extracted from the full 3D model fields. All BC, OA and $SO_4$ aerosol mass is assumed to be present in the fine size fraction (< 2.5 µm), whereas a factor of 0.25 for SS and 0.1 for DU has been used to calculate the approximate contribution from these components to the fine aerosol size fraction (Eq. 1).

$$PM_{2.5} = BC + OA + SO_4 + (0.25 \times SS) + (0.1 \times DU) \quad (1)$$

The factors used to calculate the contribution of SS and DU concentrations to the $PM_{2.5}$ size fraction are likely to depend on the individual aerosol scheme and the simulated aerosol size distribution within a particular model. The calculation of an approximate $PM_{2.5}$ concentration using Eq. (1) is therefore likely to introduce some errors, but it does provide an estimate that is consistent across models and also with that previously used in CMIP5 and ACCMIP (Fiore et al., 2012; Silva et al., 2013, 2017). For the CNRM-ESM2-1 model, anomalously large concentrations were obtained from the sea
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Changes in annual total (anthropogenic and biomass) global air pollutant emissions (relative to 2015) of sulphur dioxide (SO₂), organic carbon (OC), black carbon (BC), non-methane volatile organic compounds (NMVOCs), nitrogen oxides (NOₓ), carbon monoxide (CO) and global methane (CH₄) abundances in the future CMIP6 scenarios used as input to CMIP6 models. The dashed black line represents the 2015 value. Global CH₄ abundances are not reduced in the AerChemMIP ssp370-lowNTCF simulations used here.</figcaption></figure>

salt mass mixing ratios. Sensitivity tests with this model suggested that a much smaller factor of 0.01 was more appropriate to use for SS, which takes into account the non-dry nature of the sea salt aerosols and the large possible size range, up to 20 µm in diameter, of sea salt particles within the CNRM-ESM2-1 model (Pierre Nabat, personal communication, 27 November 2019).

Details of the data used in this study from different CMIP6 models, in both the historical and future scenarios, are presented in Table 1. For the historical period, data were available from 6 different CMIP6 models for O₃ and 11 models for PM₂.₅. The future scenario with the most data available was ssp370, with 6 models supplying data for O₃ and 10 models for PM₂.₅. For the other Tier 1 CMIP6 scenarios (ssp126, ssp245 and ssp585), data were only available for four models for O₃ and seven for PM₂.₅ (all components). It was decided to focus the analysis on ssp370 and other Tier 1 scenarios due to the limited availability of model data for Tier 2 scenarios (ssp119, ssp434, ssp460 and ssp534-over). The results from an O₃ parameterisation (Turnock et al., 2018, 2019), referred to in this study as HTAP_param, have also been included in the analysis of surface O₃ from CMIP6 models for both the historical and future scenarios.

The HTAP_param was previously developed based upon the source-receptor relationships of O₃ derived from perturbation experiments of regional precursor emissions and global CH₄ abundances (Wild et al., 2012; Turnock et al., 2018). The HTAP_param applies the fractional change in global CH₄ abundance and regional-emission precursors (NOₓ, CO and NMVOCs) for a particular scenario to the ozone response from each individual model used in the parameterisation. The total O₃ response is obtained by summing up the response from each of the individual models to all precursor changes across all source regions. The surface O₃ response previously calculated from the HTAP_param in both the historical and future CMIP6 scenarios is compared to that from the CMIP6 models (Turnock et al., 2019). The O₃ parameterisation does not take into account the effects of climate change on surface O₃ concentrations and therefore provides an estimate of the emission-only-driven changes to surface O₃, which we compare to the climate and Earth system models.

## 2.3 Surface observations

Present-day surface O₃ and PM₂.₅ simulated by all of the CMIP6 models are evaluated against surface observations to
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Percentage change in 2050 (circles) and 2100 (squares), relative to 2015, for annual mean total (anthropogenic and biomass) air pollutant emissions of (a) SO₂, (b) OC, (c) BC, (d) NMVOCs, (e) NOₓ and (f) CO across different world regions in the four Tier 1 future CMIP6 scenarios and the ssp370-lowNTCF scenario (identified as lowNTCF). Regions are defined in Fig. S1 in the Supplement.</figcaption></figure>

ascertain model biases and inter-model discrepancies. Surface O₃ observations are obtained from the database of the Tropospheric Ozone Assessment Report (TOAR; Schultz et al., 2017). The TOAR database provides a gridded product of surface O₃ observations over the period 1970 to 2015. The majority of measurement sites are located in North America and Europe, with a smaller number of other sites in East Asia, Australia, New Zealand, South America, Southern Africa, Antarctica and remote ocean locations. Here we compile a monthly mean climatology of all available O₃ observations over the period 2005–2014 from measurement locations that are classified as rural in the TOAR database (Schultz et al., 2017). The rural locations were selected to be representative of background (i.e. non-urban) O₃ concentrations and are considered to be more appropriate in evaluating the simulated values obtained at the relatively coarse horizontal resolution of the global ESMs. Simulated surface O₃ concentrations from the CMIP6 models are re-gridded onto the same resolution of the observational product (2° × 2°) for evaluation purposes.

Surface PM₂.₅ observations have been obtained from all of the locations compiled in the database of the Global Aerosol

Synthesis and Science Project (GASSP; http://gassp.org.uk/data/, last access: 2 July 2020, Reddington et al., 2017) to evaluate CMIP6 models. Background, non-urban PM₂.₅ data are compiled in the GASSP database from three major networks: the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in North America, the European Monitoring and Evaluation Programme (EMEP), and Asia-Pacific Aerosol Database (A-PAD). Again, like for O₃, the networks and observations for PM₂.₅ were selected to be representative of non-urban environments, which are more appropriate for the evaluation of global ESMs. With the exception of the IMPROVE network, most measurements of PM₂.₅ began after the year 2000. Like for O₃, we compile a monthly mean climatology of PM₂.₅ but now over the period of 2000 to 2010, selected because the GASSP database contained the most observations within this period. Simulated surface PM₂.₅ was computed from CMIP6 models over the same time period as the observations and linearly interpolated to each measurement location. Whilst the surface observations measure total PM₂.₅ mass, the computed PM₂.₅ from CMIP6 models uses Eq. (1) and does not include all observable PM₂.₅ aerosol components (e.g. nitrate aerosol).
<table><caption>**Table 1.** Number of ensemble members used for the historical- and future-scenario experiments from each model in the analysis of surface O₃ and PM₂.₅ in this study.</caption><thead><tr><th>Model</th><th>Pollutant</th><th>Historical</th><th>ssp126</th><th>ssp245</th><th>ssp370</th><th>ssp370-lowNTCF</th><th>ssp585</th><th>Model references</th><th>Data citation</th></tr></thead><tbody><tr><td>BCC-ESM1</td><td>O<sub>3</sub>, PM<sub>2.5</sub></td><td>3</td><td></td><td>3</td><td>3</td><td></td><td></td><td>Wu et al. (2019, 2020)</td><td>Zhang et al. (2018, 2019)</td></tr><tr><td>CESM2-WACCM</td><td>O<sub>3</sub>, PM<sub>2.5</sub></td><td>3</td><td></td><td>1</td><td>1</td><td></td><td></td><td>Gettelman et al. (2019), Tilmes et al. (2019), Emmons et al. (2020)</td><td>Danabasoglu (2019a-c)</td></tr><tr><td>CNRM-ESM2-1</td><td>PM<sub>2.5</sub></td><td>3</td><td></td><td>3</td><td>3</td><td></td><td></td><td>Michou et al. (2019), Séférian et al. (2019)</td><td>Seferian (2018, 2019), Voldoire (2019)</td></tr><tr><td>GFDL-ESM4</td><td>O<sub>3</sub>, PM<sub>2.5</sub></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>Horowitz et al. (2020), Dunne et al. (2020)</td><td>Horowitz et al. (2018), John et al. (2018), Krasting et al. (2018)</td></tr><tr><td>HadGEM3-GC31-LL</td><td>PM<sub>2.5</sub></td><td>4</td><td>1</td><td>1</td><td></td><td></td><td>1</td><td>Kuhlbrodt et al. (2018)</td><td>Ridley et al. (2018); Good (2019)</td></tr><tr><td>MIROC6-ES2L</td><td>PM<sub>2.5</sub></td><td>3</td><td>1</td><td>1</td><td>1</td><td></td><td>1</td><td>Takemura (2012), Hajima et al. (2020)</td><td>Hajima and Kawamiya (2019), Tachiiri and Kawamiya (2019)</td></tr><tr><td>MPI-ESM1.2-HAM</td><td>PM<sub>2.5</sub></td><td>1</td><td></td><td></td><td>1</td><td>1</td><td></td><td>Tegen et al. (2019)</td><td>Neubauer et al. (2019)</td></tr><tr><td>MRI-ESM2-0</td><td>O<sub>3</sub>, PM<sub>2.5</sub></td><td>5<br>5</td><td>1<br>1</td><td>1<br>1</td><td>3<br>3</td><td>1<br>1</td><td>1<br>1</td><td>Yukimoto et al. (2019d), Os-<br>hima et al. (2020)</td><td>Yukimoto et al. (2019a-c)</td></tr><tr><td>GISS-E2-1-G</td><td>O<sub>3</sub>, PM<sub>2.5</sub></td><td>5<br>4</td><td>1<br>1</td><td>5<br>5</td><td>1<br>1</td><td></td><td>1<br>1</td><td>Bauer et al. (2020)</td><td>NASA Goddard Institute For<br>Space Studies (NASA/GISS;<br>2018)</td></tr><tr><td>NorESM2-LM</td><td>PM<sub>2.5</sub></td><td>1</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>Karset et al. (2018), Kirkevåg<br>et al. (2018)</td><td>Norwegian Climate Center<br>(NCC; 2018)</td></tr><tr><td>UKESM1-0-LL</td><td>O<sub>3</sub>, PM<sub>2.5</sub></td><td>5</td><td>5</td><td>5</td><td>5</td><td>3</td><td>5</td><td>Sellar et al. (2019)</td><td>Good et al. (2019); Tang et al.<br>(2019)</td></tr><tr><td>Total number of models</td><td>O<sub>3</sub><br>PM<sub>2.5</sub></td><td>6<br>11</td><td>4<br>7</td><td>4<br>7</td><td>6<br>10</td><td>5<br>8</td><td>4<br>7</td><td></td><td></td></tr></tbody></table>

Therefore, it is anticipated that the CMIP6 models will underrepresent the PM₂.₅ observations in this comparison.

To address the anticipated disparity between the observed ground-based PM₂.₅ and the approximate PM₂.₅ from CMIP6 models, a further comparison has been made between the CMIP6 models and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), aerosol reanalysis product (Buchard et al., 2017; Randles et al., 2017). The MERRA-2 aerosol product assimilates observations of aerosol optical depth (AOD) from ground-based and satellite remote-sensing platforms into model simulations that use the GEOS-5 atmospheric model coupled to the GOCART aerosol module. The data assimilation used in MERRA-2 generally improves comparisons of PM₂.₅ with observations, but there are still overestimations due to dust and sea salt and underestimations over East Asia (Buchard et al., 2017; Provençal et al., 2017). Separate mass mixing ratios for BC, OA, SO₄, SS and DU aerosol components are provided from MERRA-2, which are then combined using the formula in Eq. (1) to make an approximate PM₂.₅. Monthly mean approximate PM₂.₅ concentrations are then computed over the period 2005–2014 from the MERRA-2 reanalysis product to provide a more direct comparison and enhanced spatial coverage against the approximate PM₂.₅ concentrations calculated from the CMIP6 models calculated over the same time period.

# 3 Present-day model evaluation of air pollutants

## 3.1 Surface ozone

The six CMIP6 models with data available for the historical experiments are evaluated against surface O₃ observations from the TOAR database over the period 2005–2014. A long-term evaluation of surface O₃ concentrations from CMIP6 models using observations compiled over the 20th century is presented separately in Griffiths et al. (2020). Figure 3 shows the annual and seasonal multi-model mean in surface O₃ over the period 2005–2014 and the SD across the six CMIP6 models. The annual and seasonal mean surface O₃ concentrations and evaluation against observations for individual CMIP6 models are shown in Figs. S2–S7. Higher surface O₃ concentrations are simulated in the Northern Hemisphere summer (June, July, August–JJA) when O₃ formation is enhanced by increased photolytic activity and levels of oxidants as well as larger biogenic emissions. The hemispheric difference in surface O₃ is smaller in December, January and February (DJF) when O₃ production is less in the Northern Hemisphere but higher in the Southern Hemisphere. However, model diversity is larger in DJF (Fig. 3e) due to individual models simulating different seasonal cycles of O₃, particularly UKESM1-0-LL which has the most pronounced seasonal cycle of all 6 models (Fig. S2).

The multi-model mean of CMIP6 models overestimates surface O₃ concentrations by up to 16 ppb annually and in
both seasons when compared to observations from the TOAR database, although they do capture the broad hemispheric gradient in O₃ concentrations (Fig. 3c, f and i). The model observational comparison of CMIP6 models to the TOAR observations is consistent across all models and with the previous evaluation of ACCMIP models (Young et al., 2018). This indicates a common source of error within models, for example uncertainties in emission inventories, deposition processes or vertical mixing (Wild et al., 2020). In addition, the coarse resolution of the ESMs could lead to an overproduction of O₃ across polluted regions, with finer resolutions exhibiting improvements in the simulation of surface O₃ (Wild and Prather, 2006; Neal et al., 2017). Smaller model biases exist in DJF (< 5 ppb) than in JJA (5–15 ppb), mostly attributed to the strong seasonal cycle simulated by UKESM1-0-LL. In contrast to other models (Figs. S2–S7), UKESM1-0-LL underpredicts surface O₃ in DJF over most continental Northern Hemisphere locations, potentially indicating there is excessive NOₓ titration of O₃ in this model, which is also shown by the large sensitivity of O₃ formation to NOₓ concentrations over the historical period (Fig. S17).

The observed annual cycle in surface O₃ averaged across measurement locations within different regions is compared to that simulated by CMIP6 models (Fig. 4). Across most regions, the mean annual cycle from CMIP6 models compares relatively well to that observed. The overprediction of surface O₃ values in JJA is evident across most regions, as are the large concentrations in BCC-ESM1 and GISS-E2-1-G and the strong seasonal cycle in UKESM1-0-LL across Northern Hemisphere continental regions. Additionally, the timing of peak O₃ over continental Northern Hemisphere locations occurs earlier in the observations (springtime) than in the CMIP6 models (spring and summer), which is consistent with that from ACCMIP models (Young et al., 2018). At oceanic observation locations, surface O₃ is overestimated in CMIP6 models by up to 20 ppb across all seasons, indicating that the O₃ deposition rate could be underestimated here (Clifton et al., 2020). There is also a large overestimation (~20 ppb) in all models at the one observation location in South East Asia, potentially due to difficulty in simulating O₃ in the maritime continental boundary layer using lower-resolution global ESMs. In contrast to this, CMIP6 models, particularly UKESM1-0-LL and GISS-E2-1-G, tend to underpredict the observed surface O₃ concentrations at locations in the South Pole region in JJA by ~5 ppb. This could be due to lack of long-range transport of O₃ to these sites, inaccuracies in Southern Hemisphere precursor emissions or because of the difficulty in simulating O₃ concentrations at the appropriate elevation of measurement sites located on the Antarctic Ice Sheet.

## 3.2 Surface PM₂.₅

### 3.2.1 Ground-based observations

A similar comparison is made for annual and seasonal mean surface PM₂.₅ concentrations from CMIP6 models against ground-based surface observations (Fig. 5). The annual and seasonal multi-model mean from CMIP6 models shows that elevated PM₂.₅ concentrations (> 50 µg m⁻³) occur close to the large dust emission source regions of the Sahara and Middle East in both DJF and JJA over 2000–2010. These natural source regions are also one of the largest areas of diversity in PM₂.₅ concentrations (up to 20 µg m⁻³) between the different CMIP6 models (Fig. 5b, e, h and Fig. S8). High concentrations of PM₂.₅ (> 40 µg m⁻³) are also simulated over the large anthropogenic source regions of South and East Asia, particularly in DJF, when there is enhanced variability across CMIP6 models due to the different contribution from anthropogenic PM₂.₅ components (Figs. S9–S11). The diversity in the CMIP6 models is particularly evident in the organic-aerosol concentrations across Asia, with higher present-day values simulated by CESM2-WACCM and UKESM1-0-LL and lower values in CNRM-ESM2-1 and MIROC-ES2L (Fig. S11). Lower PM₂.₅ concentrations (< 10 µg m⁻³) are predicted across both North America and Europe, with more agreement between CMIP6 models. Across the biomass-burning regions of South America and Southern Africa, PM₂.₅ concentrations are elevated in JJA, with larger diversity in the CMIP6 models due to the differing contributions of the BC and OA components, particularly shown in NorESM2-LM, GISS-E2-1-G and GFDL-ESM4 (Figs. S10 and S11). Relatively consistent PM₂.₅ concentrations of < 10 µg m⁻³, with small model diversity (< 5 µg m⁻³), are shown across oceanic regions, mainly from emissions of sea salt (Fig. S12). Apart from the natural sources of aerosol, which are subject to meteorological variability, the CMIP6 models are relatively consistent when simulating PM₂.₅ concentrations across most regions.

Compared to the ground-based observations from the GASSP database, the CMIP6 multi-model mean underpredicts the observed PM₂.₅ values by up to 10 µg m⁻³ in both seasons, with a slightly larger underestimation in DJF than JJA. As discussed in Sect. 2.3, an underestimation was anticipated from comparing approximate PM₂.₅ concentrations derived from CMIP6 models to observed values. Nevertheless, the evaluation highlights that fine particulate matter (PM₂.₅) is generally underrepresented in the CMIP6 models across North America, Europe and parts of Asia for which observations are available, a similar result to other studies evaluating global and regional models (Tsigaridis et al., 2014; Pan et al., 2015; Glotfelty et al., 2017; Solazzo et al., 2017; Im et al., 2018). Numerous reasons potentially exist for the model observation discrepancy shown here and in other studies, including uncertainties in emission inventories (e.g. local dust sources), errors in the wet and dry deposition schemes,
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Multi-model (six CMIP6 models) annual and seasonal mean surface O₃ concentrations over the 2005–2014 period in (a) annual mean; (d) December, January and February (DJF); and (g) June, July and August (JJA). The SD of the multi-model mean in (b) annual mean, (e) DJF and (h) JJA. The difference between the multi-model mean and TOAR observations in (c) annual mean, (f) DJF and (i) JJA (colour bar saturates).</figcaption></figure>

the absence or underrepresentation of aerosol formation processes (e.g. secondary organic aerosols), and the coarse resolution of global models leading to errors in emissions and simulated meteorology. Understanding the causes of model observational discrepancies is an area of active research and should be explored in further research, for example in a global multi-model sensitivity study that examines model uncertainties.

The simulated regional mean annual cycle in surface PM2.5 from different CMIP6 models against observations is shown in Fig. 6. The low model bias in PM2.5 concentrations is highlighted across all regions, except for the ocean region, where there is a relatively large diversity in model simulations, particularly MIROC-ES2L and NorESM2-LM, at these observation locations. Across North America, the region with the most observations, the annual cycle is simulated relatively well, with a peak in concentrations in JJA

and a lower model bias, although a larger model bias (factor of ~1.5 to 2) occurs in winter and spring. Across Europe, there is a larger underestimation of observed PM2.5 concentrations by CMIP6 models in DJF (factor > 2) than JJA. Nitrate aerosols are observed and modelled (from two CMIP6 models in Fig. S13) to contribute between 1 and 5 µg m⁻³ of the total aerosol mass over Europe (Fagerli and Aas, 2008; Pozzer et al., 2012), explaining part, but not all, of the model observational discrepancy here. Additionally, in Fig. 6 the CMIP6 models also underestimate the MERRA-2 reanalysis product (which does not include nitrate aerosols), indicating that other aerosol sources and processes are underrepresented across Europe and other regions in the models. The limited number of observations across other regions makes it difficult to infer particular model observational biases. However, over Asia CMIP6 PM2.5 concentrations tend to be within a factor of 2 of the observations and represent the seasonal
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Individual and multi-model (six CMIP6 models and HTAP_param) monthly mean surface O₃ concentrations across different world regions compared with the regional monthly values from all the TOAR observations within the region for the period 2005–2014. The number of observations within a region is shown in parentheses. The shading shows variability in observations across all sites within the region.</figcaption></figure>

cycle relatively well at these locations. Over Asia, larger PM₂.₅ concentrations are simulated in the CMIP6 models CESM2-WACCM, HadGEM3-GC31-LL and UKESM1-0-LL, mainly due to the larger OA component (Fig. S11). Across South Asia, concentrations are relatively well simulated in JJA, but a larger discrepancy (15 µg m⁻³) exists in DJF between the model and observations.

### 3.2.2 MERRA reanalysis product

An additional comparison of surface PM₂.₅ concentrations from the MERRA-2 aerosol reanalysis product is made with that simulated by the CMIP6 models to improve the spatial coverage and provide a more consistent evaluation of the approximate PM₂.₅ concentrations. Fig. 7 shows the same comparison as in Fig. 5 but now using the approximate PM₂.₅ obtained from the MERRA-2 reanalysis product over the period 2005–2014. In comparison to MERRA-

2, the CMIP6 models are shown to underpredict PM₂.₅ concentrations across North America, Europe and Eurasia but by a smaller amount in comparison to ground-based observations. A similar seasonal-cycle comparison is shown for Europe and North America (regions with most ground-based observations) in both Figs. 6 and 8, providing confidence that the underestimation of PM₂.₅ by CMIP6 models is robust over these regions. Across all other regions, the MERRA-2 reanalysis product provides much greater spatial coverage for each region, and therefore the features shown in the site-level comparison (Fig. 6) will not necessarily apply here. A large overestimation of the MERRA-2 reanalysis product by the CMIP6 multi-model mean is shown across East and South Asia. Figure 8 shows that on a regional mean basis, most CMIP6 models are within the spread of the MERRA-2 concentrations for East Asia, although MERRA-2 was previously shown to underestimate PM₂.₅ concentrations across East Asia (Buchard et al., 2017; Provençal et al., 2017) and
<figure><img src="image_7.png" /><figcaption>(b) Annual PM<sub>2.5</sub> Standard deviation</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(c) Annual surface PM<sub>2.5</sub> Bias from Multi-model Mean</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(d) Surface PM<sub>2.5</sub> from Multi-model Mean in DJF</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(e) PM<sub>2.5</sub> Standard deviation in DJF</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(f) Surface PM<sub>2.5</sub> Bias from Multi-model Mean in DJF</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(g) Surface PM<sub>2.5</sub> from Multi-model Mean in JJA</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(h) PM<sub>2.5</sub> Standard deviation in JJA</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(i) Surface PM<sub>2.5</sub> Bias from Multi-model Mean in JJA</figcaption></figure>

<figure><img src="image_15.png" /></figure>

**Figure 5.** Multi-model (11 CMIP6 models) annual and seasonal mean surface PM<sub>2.5</sub> concentrations over the 2000–2010 period in (a) annual mean; (d) December, January and February (DJF); and (g) June, July and August (JJA). The SD of the multi-model mean in (b) annual mean, (e) DJF and (h) JJA. The difference between the multi-model mean and PM<sub>2.5</sub> observations in (c) annual mean, (f) DJF and (i) JJA (colour bar saturates).

also in Fig. 6. CESM2-WACCM and MRI-ESM2-0 are the exceptions to this, with distinctly higher PM<sub>2.5</sub> concentrations over East Asia, potentially due to larger OA concentrations and more dust aerosols within the western side of this region (Figs. S8 and S11). Across the South Asian region, CMIP6 models consistently overestimate MERRA-2 by more than 10 µg m<sup>-3</sup> in certain months. UKESM1-0-LL, MRI-ESM2-0 and CESM2-WACCM simulate particularly high monthly PM<sub>2.5</sub> concentrations of 20–40 µg m<sup>-3</sup> over South Asia due to large contributions from SO<sub>4</sub>, dust and OA. Across North Africa there is considerable variability in PM<sub>2.5</sub> within this region as CMIP6 models both under- and overestimate the MERRA-2 PM<sub>2.5</sub> concentrations, although this results in a relatively good regional mean representation (Figs. 7 and 8). The annual mean cycle in MERRA-2 PM<sub>2.5</sub> concentrations across South America is well represented by the CMIP6 models, although the peak in the

biomass-burning season is underestimated by 5–10 µg m⁻³ in some models. A more pronounced annual cycle is exhibited by UKESM1-0-LL across Southern Africa due to the larger contributions from the OA fraction (Fig. S11), potentially from enhanced biogenic emissions that result in secondary OA formation (SOA). Across oceanic locations all of the CMIP6 models underestimate the MERRA-2 PM<sub>2.5</sub> concentrations by 5 µg m⁻³, although MERRA-2 was previously shown to overestimate sea salt concentrations (Buchard et al., 2017; Provençal et al., 2017), accounting for some of this discrepancy. Overall, comparisons of CMIP6 models with the MERRA-2 reanalysis product show biases across Europe and North America that are consistent with the comparison to ground-based observations. Additionally, similar comparisons are shown in annual mean cycles across other regions, for which appropriate ground-based data are lacking.
<figure><img src="image_16.png" /><figcaption>**Figure 6.** Individual and multi-model (11 CMIP6 models) monthly mean surface PM<sub>2.5</sub> concentrations across different world regions compared with the regional monthly values from all the PM<sub>2.5</sub> observations (◇) and the MERRA-2 reanalysis product (×) within the region for the period 2000–2010. The number of observations within the region is shown in parentheses. The shading and errors bars show variability in observations and the reanalysis product across all sites within the region.</figcaption></figure>

# 4 Air pollutants from the pre-industrial period to present day

## 4.1 Surface ozone

The simulated changes in surface O<sub>3</sub> across six CMIP6 models and the HTAP_param are shown in Fig. 9 and Figs. S14–S15 over the historical period of 1850 to 2014. The CMIP6 multi-model mean shows that global annual mean surface O<sub>3</sub> has increased by 11.7 ± 2.3 ppb since 1850 (±1 SD), although the change could be as large as 14 ppb (from BCC-ESM1) or as little as 7 ppb (from UKESM1-0-LL). Globally and over most regions there has been a larger historical increase in surface O<sub>3</sub> in JJA than in DJF (Fig. S16). The 1850 to 2000 multi-model annual mean change in surface O<sub>3</sub> from the CMIP6 models of 10.6 ppb is in good agreement with the 10 ± 1.6 ppb simulated by the CMIP5 models used in AC-

CMIP (Young et al., 2013). An evaluation of the long-term changes in surface O<sub>3</sub> over the historical period simulated by the CMIP6 models at specific measurement locations is presented separately in the tropospheric O<sub>3</sub> CMIP6 companion paper of Griffiths et al. (2020). This shows that CMIP6 models can reasonably represent long-term changes in surface ozone since the 1960s, providing a degree of confidence in the future projections of changes in the CMIP6 scenarios. However, long-term changes in simulated surface O<sub>3</sub> from the previous generation of global coupled chemistry–climate models (used in CMIP5) were found to underestimate the observed trend at Northern Hemisphere monitoring locations (Parrish et al., 2014). Further comparisons of historical surface O<sub>3</sub> simulated by CMIP6 models with long-term historical observations are outside the scope of the current work but will be the subject of future research.
<figure><img src="image_17.png" /><figcaption>(b) Annual PM<sub>2.5</sub> standard deviation</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(c) Annual surface PM<sub>2.5</sub> Bias against MERRA</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(d) Surface PM<sub>2.5</sub> from Multi-model Mean in DJF</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(e) PM<sub>2.5</sub> Standard deviation in DJF</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>(f) Surface PM<sub>2.5</sub> Bias against MERRA in DJF</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(g) Surface PM<sub>2.5</sub> from Multi-model Mean in JJA</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(h) PM<sub>2.5</sub> Standard deviation in JJA</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>(i) Surface PM<sub>2.5</sub> Bias against MERRA in JJA</figcaption></figure>

<figure><img src="image_25.png" /></figure>

**Figure 7.** Multi-model (11 CMIP6 models) annual and seasonal mean surface PM<sub>2.5</sub> concentrations over the 2005–2014 period in (a) annual mean; (d) December, January and February (DJF); and (g) June, July and August (JJA). The SD of the multi-model mean in (b) annual mean, (e) DJF and (h) JJA. The difference between the multi-model mean and MERRA-2 reanalysis for (c) annual mean, (f) DJF and (i) JJA.

A large diversity in the simulated historical changes is shown across the different regions analysed here, with UKESM1-0-LL tending to simulate the smallest historical change and GISS-E2-1-G or BCC-ESM1 the largest. The large diversity across CMIP6 models in the surface O₃ response over the historical period can be attributed to the different magnitude of simulated O₃ concentrations in the 1850 period (Fig. S14) and the rate of change in regional mean O₃ concentrations (Fig. S15), which is related to the different chemical sensitivity of O₃ formation in each model to changing NOₓ concentrations (Fig. S17). Larger differences between CMIP6 models are shown in the DJF mean historical changes over Northern Hemisphere regions than what occurred in JJA (Fig. S16), reflecting the differences shown in the model evaluation (Fig. 4) and the strong seasonality of the changes. Even though the historical surface O₃ response is small in UKESM1-0-LL, it is shown to have larger tropo-

spheric changes in O₃ over the historical period compared to other CMIP6 models (Griffiths et al., 2020).

South Asia is the region with the largest diversity in simulated historical changes in surface O₃, between 16 and 40 ppb, with a larger range in DJF (10–40 ppb) than in JJA (19–36 ppb). The large diversity in CMIP6 models is attributed to the large differences in simulated NOₓ concentrations and hence chemical sensitivities of O₃ formation occurring across South Asia over the historical period (Fig. S17). In addition, the large historical change in PM₂.₅ over this region (Fig. S18) could alter the heterogeneous loss rate of radicals to aerosols and therefore also affect O₃ formation. Surface O₃ is simulated to have increased by between 10 and 30 ppb on an annual mean basis and by a larger amount in JJA (12 to 37 ppb) over the major northern anthropogenic source regions since 1850, driven mainly by the large increases in anthropogenic precursor emissions of CH₄, NOₓ, CO, and NMVOCs over this period.
<figure><img src="image_26.png" /><figcaption>**Figure 8.** Individual and multi-model (11 CMIP6 models) monthly mean surface PM<sub>2.5</sub> concentrations across different world regions compared with the regional monthly values from the PM<sub>2.5</sub> MERRA-2 reanalysis within the region for the period 2005–2014. The number of reanalysis points within the region is shown in parentheses. The shading shows variability in the values of the MERRA-2 reanalysis products across the region.</figcaption></figure>

A qualitative estimate of the influence of non-emission-
driven processes (chemistry and climate change) can be as-
certained by comparing results from the HTAP_param, an
emission-only-driven model, to those of the CMIP6 models.
Simulated historical changes in surface O<sub>3</sub> from UKESM1-
0-LL are comparable to those from the HTAP_param, indi-
cating that the magnitude of change simulated by UKESM1-
0-LL is similar to that solely from changes in precursor emis-
sions. However, the global annual mean surface O<sub>3</sub> response
of 7.6 ± 0.7 ppb from HTAP_param over the historical pe-
riod is 4.1 ppb lower than the CMIP6 multi-model mean,
indicating globally that non-emission-driven processes have
contributed to approximately 30% of the change in surface
O<sub>3</sub>, although this contribution varies regionally. The differ-
ent magnitude of response across models could be due to
non-emission-driven processes, e.g. from different chemistry
schemes and climate change signals within models.

4.2 Surface PM<sub>2.5</sub>

The simulated change in annual mean surface PM2.5 across 11 CMIP6 models is shown in Fig. 10 over the historical period of 1850 to 2014. CMIP6 models simulated an increase in global annual and seasonal mean surface PM2.5 concentrations of < 2 µg m-3 (15%-20%) since 1850. Larger regional increases in surface annual mean PM2.5 of up to 12 µg m-3 are simulated across South and East Asia, with changes in DJF (up to 21 µg m-3) larger than those in JJA (up to 12 µg m-3; Fig. S16), reflecting the strong seasonality of PM2.5 concentrations in these regions. The historical increase in surface PM2.5 is primarily driven by the large increase in anthropogenic aerosol and aerosol precursor emissions over the 1850–2014 period (Hoesly et al., 2018). The largest model diversity is also exhibited over the Asian regions, with variations in the response between models of
<figure><img src="image_27.png" /><figcaption>**Figure 9.** Changes in the regional and global annual mean surface O₃ concentrations, relative to a 2005–2014 mean value, across six CMIP6 models and the HTAP_param. The multi-model annual mean 2005–2014 surface O₃ concentrations (±1 SD) are shown in the top left of each panel. Regions are defined in Fig. S1.</figcaption></figure>

up to 50% and larger differences between models in DJF than JJA (Fig. S16), reflecting the differences shown in the present-day model evaluation (Fig. 6). The inter-model differences can be attributed to the different simulation of historical changes in the anthropogenic components sulphate, black carbon and organic aerosols (Fig. S18). The largest inter-annual variability in surface PM₂.₅ concentrations occurs over the North African and Middle East regions as they are located near large sources of dust, whose emissions are highly dependent on meteorological fluctuations (wind speed). Over Europe and to a lesser extent Russia, Belarus, Ukraine and North America, the increase in surface PM₂.₅ concentrations since 1850 peaked in the 1980s at 4 µg m⁻³ above the 2005–2014 mean value before decreasing over the last 30 years. There are limited long-term multi-decadal observational data available to assess changes in aerosols simulated by global models. Previous studies using long-term data since the 1980s, mainly over Europe and North America, have found that global models are able to reproduce the

observed multi-decadal changes in aerosols relatively well (Pozzoli et al., 2011; Leibensperger et al., 2012; Tørseth et al., 2012; Chin et al., 2014; Turnock et al., 2015; Aas et al., 2019). More recently, global composition models, including some CMIP6 models, were shown to be able to reproduce the observed changes in AOD, sulphate and particulate matter over the last 2 decades (Mortier et al., 2020). The ability of global composition models to reproduce historical changes in aerosols provides a degree of confidence in the future projections under the CMIP6 scenarios. Further model observational comparisons of multi-decadal changes in aerosols will need to be undertaken to improve the understanding of changing aerosol properties and processes.

## 5 Air pollutants from present day to 2100

An analysis is now made of the future projections of air pollutants in the CMIP6 Tier 1 scenarios, including ssp370-
<figure><img src="image_28.png" /><figcaption>**Figure 10.** Changes in the regional and global annual mean surface PM<sub>2.5</sub> concentrations, relative to a 2005–2014 mean value, across 11 CMIP6 models. Changes for each region are computed as 10-year running means over the historical period. The multi-model mean 2005–2014 surface PM<sub>2.5</sub> concentrations (±1 SD) are shown in the top left of each panel. Regions are defined in Fig. S1.</figcaption></figure>

lowNTCF. A comparison is made of the projected future changes in 2050 and 2100 from the four CMIP6 models (CESM2-WACCM, GFDL-ESM4 and UKESM1-0-LL for both O<sub>3</sub> and PM<sub>2.5</sub> along with BCC-ESM1 for O<sub>3</sub> and MIROC-ES2L for PM<sub>2.5</sub>) that had the most data available for the ssp370 scenario.

## 5.1 Surface ozone

Global annual mean surface O<sub>3</sub> is reduced by more than 5 ± 1.2 ppb (±1 SD value of the multi-model mean) in the near-term (2050) and by 9 ± 1.6 ppb in 2100 in the strong air pollutant and climate mitigation scenario ssp126 (Fig. 11). Smaller reductions in global annual mean surface O<sub>3</sub> of 4 ± 1.7 ppb are predicted for the middle-of-the-road pathway (ssp245) by 2100, whereas for the weak climate and air pollutant mitigation scenario ssp370, a global annual mean increase in surface O<sub>3</sub> of 1.6 ± 0.9 ppb in 2050 and 0.6 ± 1.0 ppb by 2100 is predicted. However, implementing strong emis-

sion controls for SLCFs on top of a weak climate mitigation scenario (ssp370-lowNTCF) shows that previous increases in global annual mean surface O<sub>3</sub> can be substantially reduced to values that are 2.5 ± 0.5 ppb below the 2005–2014 mean value in 2050, with benefits to air quality and climate (Allen et al., 2020). For ssp585, which has weak climate mitigation measures but strong air pollution controls, a near-term increase in global annual mean surface O<sub>3</sub> of 1.4 ± 0.8 ppb is predicted in 2050, but by 2100 surface O<sub>3</sub> reduces by 2.7 ± 1.5 ppb, relative to 2005–2014, due to the implementation of air pollutant controls in the latter half of the 21st century.

The global response in annual mean surface O<sub>3</sub> concentrations to the different scenarios is also repeated across the different world regions, albeit with differing magnitudes. In ssp370, increases in annual mean surface O<sub>3</sub> are predicted to occur across North America (+1.6 ppb), Europe (+5.4 ppb) and East Asia (+5.9 ppb), with the largest increase, of 15.1 ± 9.6 ppb, predicted in South Asia by 2100. Despite the re-
ductions in O₃ precursor emissions across North America, Europe and East Asia by 2100 (Fig. 2), surface O₃ concentrations have continued to increase up to the end of this period, indicating the importance of future changes in chemistry, global CH₄ abundances and climate to the response of surface O₃ in ssp370 (Wild et al., 2012; Gao et al., 2013; Rasmussen et al., 2013; Young et al., 2013; Colette et al., 2015; Fortems-Cheiney et al., 2017; Li et al., 2019; Turnock et al., 2019). South Asia shows the largest increase in surface O₃ as precursor emissions are anticipated to increase across this region on top of the large climate change signal and growth in CH₄ abundance. Additionally, the largest diversity in projections between the CMIP6 models is shown over South Asia, indicating that there is some disagreement between the models as to the magnitude and extent of changes over this region. Surface O₃ across oceanic regions (background) is predicted to remain at or near current values in ssp370 due to the increases in water vapour in a warming world, leading to more O₃ destruction (Johnson et al., 1999; Doherty et al., 2013). The impact of more aggressive near-term reductions to emissions of SLCFs (but not CH₄) on top of the ssp370 pathway is shown by the smaller changes in the ssp370-lowNTCF (Figs. 11 and S19–S20 for individual models). In this pathway, surface O₃ concentrations are reduced globally and across most regions to be at or near 2005–2014 values, a substantial benefit to surface O₃ air quality compared to ssp370. Surface O₃ concentrations are predicted to have almost halved by 2050 across South Asia in ssp370-lowNTCF. However, across East Asia the additional precursor emission reductions in ssp370-lowNTCF have resulted in smaller benefits to surface O₃ concentrations being simulated by the CMIP6 models than in other regions (Fig. S20), which is attributed to an increase in surface O₃ concentrations over eastern China (a part of the larger East Asian region shown in Fig. S1). This increase in surface O₃ results from the slight increase in NMVOC emissions (Fig. 2) and a reduction in the NOₓ titration of O₃ due to the large decreases in NOₓ emissions in ssp370-lowNTCF. In addition, a reduction in the heterogeneous loss of radicals due to decreases in PM₂.₅ concentrations in ssp370-lowNTCF could also lead to increased surface O₃ concentrations (Li et al., 2019).

Surface O₃ concentrations predicted across Northern Hemisphere regions in ssp585 are similar to ssp370 due to comparable changes in air pollutant emissions and climate change. However, a notable exception is a reduction in surface O₃ across regions towards the latter half of the 21st century (post-2080), when there are additional reductions in precursor emissions and global CH₄ abundances by 2100. Surface O₃ shows a slightly slower increase until the mid-21st century over South Asia in ssp585 than what occurred in ssp370. This can be attributed to a slightly different temporal evolution of NOₓ emissions over this region in that they peak earlier (by 2040) and decline more rapidly in ssp585 when compared to the continual increase in NOₓ emissions in ssp370 (Fig. 2), which results in a different response of O₃

formation within CMIP6 models. In addition, there are more CMIP6 models with data available for ssp370 (six models) than ssp585 (four models; Table 1), which could affect the multi-model mean response shown in Fig. 11.

The future scenario ssp245 (middle of the road) predicts annual mean surface O₃ concentrations that tend to remain at or near the 2005–2014 mean values by 2100 across the major anthropogenic source regions of the Northern Hemisphere, whereas for other tropical and Southern Hemisphere regions surface O₃ concentrations are reduced by more than 4 ppb. The changes in ssp245 are driven by larger precursor emission controls, a smaller climate change signal and controlling CH₄ so that global abundances are below 2015 values by 2100 (Fig. 1g). In ssp245 a near-term (up to 2040) increase in surface O₃ is shown across East Asia and South Asia, which could be attributed to the peaking of global CH₄ abundances at this point prior to then reducing.

The Tier 1 future scenario with the strongest climate and air pollutant mitigation measures, ssp126, shows substantial decreases in surface O₃ concentrations across most regions due to the large reduction in precursor emissions, global CH₄ abundances and small climate change signal. Reductions in surface O₃ of more than 10 ppb are predicted across anthropogenic-emission source regions of the Northern Hemisphere, with smaller reductions across Southern Hemisphere regions.

Projections from the CMIP6 models show that to achieve global benefits for regional surface O₃, it is important to control O₃ precursor emissions (including CH₄) in addition to limiting future climate change. However, scenarios with large climate change signals (ssp370 and ssp585) but different post-2050 controls on O₃ precursors (most notably CH₄ and NOₓ) show different long-term changes in regional surface O₃ concentrations, which could have important consequences for any potential human health impacts.

A more detailed comparison of future surface O₃ projections between CMIP6 models has been undertaken for ssp370 as it is the scenario with the largest number of available models (Table 1). The regional change in annual and seasonal mean surface O₃, relative to 2005–2014, in 2050 (2045–2055 mean) and 2095 (2090–2100 mean) for ssp370 from four CMIP6 models and the HTAP_param is shown in Fig. 12. An analysis of the relationships, in terms of correlation coefficients, between future annual mean surface O₃ concentrations and other variables (CH₄ concentrations, surface air temperature, NOₓ concentrations, emissions of BVOCs and anthropogenic emissions of NMVOCs) is undertaken for CMIP6 models in the ssp370 scenario (Fig. 13). Discrepancies in the simulated response of background O₃ across the ocean region (as well as the South Pole and Pacific, Australia and New Zealand) are noticeable between individual models, with UKESM1-0-LL predicting a decrease in surface O₃ compared to the small increase from the HTAP_param and most other models in both 2050 and 2095 (Fig. S19). The future surface O₃ response in UKESM1-0-
<figure><img src="image_29.png" /><figcaption>**Figure 11.** Future global and regional changes in annual mean surface O₃, relative to 2005–2014 mean, for the different SSPs used in CMIP6. Each line represents a multi-model mean across the region, with shading representing the ±1 SD of the mean. See Table 1 for details of models contributing to each scenario. The multi-model regional mean value (±1 SD) for the years 2005–2014 is shown in the top left corner of each panel.</figcaption></figure>

LL over the ocean region exhibits a large negative correlation with surface temperature changes (Fig. 13), indicating the importance of future climate change in this model over remote regions. UKESM1-0-LL is a model with high equilibrium climate sensitivity (ECS; 5.4 K) compared to other CMIP6 models (Forster et al., 2019; Sellar et al., 2019) and therefore will exhibit a larger climate response (surface temperature and water vapour), leading to enhanced background O₃ destruction via water vapour and the hydroxyl radical (OH). Over the North Pole region all models show surface O₃ increases that are larger than the HTAP_param, with a larger increase in DJF than JJA. The large future temperature response over the Arctic and changes in NOₓ concentrations and emissions of NMVOCs are particularly important drivers of surface O₃ changes across most CMIP6 models in this region with comparatively low local emissions (Fig. 13).

Differences in the predicted surface O₃ between models exist across South Asia, where CESM2-WACCM (and

BCC-ESM1 in 2050) predicts a response that is twice as large as UKESM1-0-LL and GFDL-ESM4. The lower annual mean response over South Asia in UKESM1-0-LL and GFDL-ESM4 is driven by a reduction in DJF in these models (Fig. S21), which results in the DJF change in 2050 being lower than the 2005–2014 annual mean value (Fig. 12). The large increase in NOₓ emissions in ssp370 over South Asia (~80%) has resulted in areas of NOₓ titration, particularly in DJF, near the Indo-Gangetic Plain in both UKESM1-0-LL and GFDL-ESM4, reducing surface O₃ concentrations (Figs. S19 and S21). This strong feature of NOₓ titration of O₃ in DJF is absent in both CESM2-WACCM and BCC-ESM1, resulting in larger O₃ production over South Asia. The comparison in Fig. 12 shows how the O₃ chemistry within models responds differently across a particular area in a future scenario with a large climate change signal and over a region with large increases in local precursor emissions but that all
the drivers related to regional O₃ change in South Asia are similarly important across all models (Fig. 13).

Over South America and Southern Africa, particularly the tropical areas (Fig. S19), larger future changes in surface O₃, particularly by 2100, are predicted by GFDL-ESM4 and UKESM1-0-LL than by CESM2-WACCM. These changes over South America are larger in JJA in all models, with small seasonal differences over Southern Africa. Over this region, biogenic emissions (particularly isoprene) are an important source of O₃ formation. Discrepancies in the future response of these BVOC emissions between models could be occurring due to the differing magnitudes of climate and land-use change and how they are coupled within individual CMIP6 models (Table S1), which could affect future surface O₃. Future changes in the total emissions of BVOCs and those solely from isoprene obtained from five CMIP6 models (Figs. S22 and S23) show that CESM2-WACCM has larger total BVOC emissions over the period 2005–2014 (due to the inclusion of more BVOCs), which then increase in the future ssp370 scenario, along with isoprene emissions, resulting in a smaller increase (and even decrease in some parts of the region) in O₃. In contrast, UKESM1-0-LL shows a larger increase in O₃ and a reduction in BVOC emissions, mainly from isoprene (Fig. 23), over parts of South America and tropical Africa. Figure 13 shows that there are differing relationships between future surface O₃ concentrations, BVOC emissions and NOₓ concentrations across CMIP6 models over South America and Southern Africa. Over Southern Africa, UKESM1-0-LL shows a different relationship between BVOC emissions and surface O₃ concentrations than other CMIP6 models, indicating that this could be leading to the different future O₃ response in this model over this region. Similarly, Fig. 13 shows that over South America, CESM2-WACCM has a different relationship between surface O₃ and the variables considered here than in other CMIP6 models, particularly for BVOCs, leading to the different future responses in this model over this region. Figure 13 shows that there are differences between models in the surface O₃ response over regions such as South America and Southern Africa, which are potentially linked to the land-surface response and are important to understand more in future work.

Whilst there are disagreements between models over some regions, there is also substantial consistency in the predicted increase in annual mean surface O₃ in ssp370 over North America, Europe and East Asia, which is larger than that from HTAP_param. However, BCC-ESM1 tends to predict a larger increase than the other three models, potentially due to the coarser resolution of this ESM. There are differences in simulated seasonal response across these regions, with all models showing a smaller increase in JJA than DJF across North America and Europe, whilst across East Asia there tends to be a larger future surface O₃ increase in JJA than DJF. Figure 13 shows that there is a negative correlation between surface O₃ and NOₓ concentrations as well as between

O₃ and NMVOC emissions for most CMIP6 models across these regions, reflecting that, as most anthropogenic precursor emissions (including NOₓ) decrease in this scenario (Fig. 2), surface O₃ is simulated to increase. An exception to this is across East Asia, where the increase in NMVOC emissions in ssp370 (Fig. 2) is positively correlated with surface O₃, indicating different chemical drivers of future O₃ across this region. In addition, there are positive correlations between the other variables (temperature, CH₄ and BVOCs) for most CMIP6 models, indicating that changes in climate and global CH₄ abundances are also important drivers of surface O₃ increases over these regions.

The differences between the individual CMIP6 models highlight the importance of further understanding how future O₃ chemistry is affected by changes to precursor emissions and climate. The predicted differences in models can be quite pronounced over regions like South Asia, where changes in one model can be double that of another model, which could have important consequences for the ability of models to simulate future regional air quality.

## 5.2 Surface PM₂.₅

Relatively small global changes in annual mean surface PM₂.₅ are predicted for all CMIP6 models across all scenarios (Fig. 14), with an increase in ssp370 and a reduction in the others. Small reductions in PM₂.₅ are predicted for all scenarios across Europe (0.3 to 3 µg m⁻³) and North America (0.0 to 1.3 µg m⁻³) due to the reduction in aerosol and aerosol precursor emissions. Differences in PM₂.₅ between scenarios are highlighted across a number of regions.

For the weak climate and air pollutant mitigation scenario ssp370, increases in annual mean surface PM₂.₅ are predicted across South Asia (7.3 ± 4.1 µg m⁻³ by 2050 and 3.1 ± 3.1 µg m⁻³ by 2100), South East Asia (2.7 ± 4.7 µg m⁻³ by 2100), Southern Africa (1.6 ± 3.7 µg m⁻³ by 2100), Central America (2.8 ± 3.2 µg m⁻³ by 2100) and South America (2.9 ± 3.6 µg m⁻³ by 2100). The increases in PM₂.₅ are driven mainly by the increase in aerosol and aerosol precursor emissions in this scenario (Fig. 2), shown by the positive correlations between emissions and surface PM₂.₅ in CMIP6 models across these regions (Fig. 16). However, there is a degree of uncertainty associated with all of these future projections, indicated by the large diversity across the CMIP6 models. Some of the largest predicted increases in surface PM₂.₅ occur across South Asia in ssp370, a region with already high present-day PM₂.₅ concentrations. The increase in PM₂.₅ peaks in 2050 across this region, which coincides with the increase in SO₂, BC and OC emissions before declining to 2100, when emissions reduce. Over East Asia, annual mean PM₂.₅ concentrations are simulated to remain at or near 2005–2014 values until the latter half of the 21st century, when the decrease in emissions reduces PM₂.₅ concentrations by 2.5 ± 2.7 µg m⁻³. The impact of reductions in SLCFs on top of the ssp370 scenario acts to constrain any increases
<figure><img src="image_30.png" /><figcaption>**Figure 12.** Future global and regional changes in the annual and seasonal mean surface O₃, relative to the 2005–2014 mean, for the ssp370 pathway used in CMIP6. Each black circle represents the annual mean response for an individual model in (a) 2045–2055 and (b) 2090–2100, with the coloured bars showing the SD across the annual mean. The DJF and JJA seasonal mean response averaged over the relevant 10-year period is shown by squares and triangles, respectively. The multi-model regional mean over the period 2005–2014 is given towards the left of each panel. The response from the HTAP_param in each time period is shown by the separate gold circle.</figcaption></figure>

in PM<sub>2.5</sub> concentrations to near present-day values across most regions. However, substantial reductions in PM<sub>2.5</sub> concentrations of 5.6 ± 2.0 µg m<sup>-3</sup> and 5.3 ± 2.1 µg m<sup>-3</sup> below 2005–2014 values are achieved by 2050 across East and South Asia, respectively, by implementing these measures. Due to the short lifetime of aerosols in the atmosphere, PM<sub>2.5</sub> concentrations respond rapidly to the large cuts in emissions that occur in ssp370-lowNTCF and show the benefits of targeting these emissions, although there could be a potential climate impact (Allen et al., 2020).

Reductions in annual mean surface PM<sub>2.5</sub> are simulated across all regions for ssp126, ssp245 and ssp585. Differences exist in the magnitude and timing of PM<sub>2.5</sub> reductions across regions linked to the changes in emissions. The largest reductions in PM<sub>2.5</sub> occur over South Asia in 2100 and range from 11.1 ± 2.8 µg m<sup>-3</sup> in ssp126 to 8.6 ± 2.9 µg m<sup>-3</sup> in ssp585, a substantial benefit to regional air quality. Similar benefits to PM<sub>2.5</sub> are achieved over East Asia by 2100, although the more rapid improvements occur over this region in the first part of the 21st century.

The response of PM<sub>2.5</sub> concentrations is more variable, with a larger diversity across CMIP6 models within regions

that are close to natural aerosol emission sources. This is particularly noticeable over North Africa, where the variability across CMIP6 models in dust emissions from the Saharan source region (Fig. S8) results in an uncertain PM<sub>2.5</sub> response across this region. A similar response is also exhibited across the Middle East and Central Asia. The potential influence of BVOCs on SOA formation (Figs. S22 and S26) could also be contributing to the diversity in the CMIP6 model responses across the South America and Southern Africa regions.

The CMIP6 models show that future reductions in aerosols and aerosol precursors will lead to a decrease in surface PM<sub>2.5</sub> concentrations across most world regions and a benefit to regional air quality (and human health), consistent with that from CMIP5. However, if emissions are not controlled over economically developing regions such as South America, Asia and Africa, then surface PM<sub>2.5</sub> is anticipated to increase and worsen future regional air quality. Targeting emission reductions in SLCFs in the short term shows the potential for rapid improvements in surface PM<sub>2.5</sub> and air quality.

In a similar analysis to that for surface O<sub>3</sub>, a more detailed comparison has been undertaken of four CMIP6 models predicting changes in annual and seasonal surface PM<sub>2.5</sub>
<figure><img src="image_31.png" /><figcaption>**Figure 13.** Correlation coefficients calculated when comparing future annual mean surface O₃ concentrations against individual variables of surface CH₄ concentrations, surface air temperature (TAS), emissions of biogenic volatile organic compounds (BVOCs), NOₓ (NO + NO₂) concentrations and anthropogenic emissions of non-methane volatile organic compounds (NMVOCs) from individual CMIP6 models over the period 2015 to 2100 in the ssp370 scenario.</figcaption></figure>

in 2050 and 2095 under ssp370 (Fig. 15). In addition, an analysis of the relationships, in terms of correlation coefficients, between future annual mean surface PM₂.₅ and other variables (total surface precipitation; surface air temperature; and emissions of BVOCs, SO₂, BC and organic aerosol) has been undertaken for CMIP6 models in the ssp370 scenario (Fig. 16). Small reductions in annual mean surface PM₂.₅ concentrations (<2µg m⁻³) are simulated consistently by all CMIP6 models across North America and Europe in ssp370, with larger reductions simulated in DJF than JJA. The reductions in annual mean PM₂.₅ over Europe and North America are mainly attributed to decreases in the BC and SO₄ components (Figs. S24 and S25), as indicated by the strong correlations with BC and SO₂ emissions across CMIP6 models (Fig. 16). However, by 2095 a small increase (up to 2µg m⁻³) is simulated in JJA by UKESM1-0-LL and CESM2-WACCM over North America, which could be attributed to changes in climate due to the strong positive cor-

relations in both models for temperature, precipitation and BVOCs (Fig. 16).

South Asia, the region with the largest simulated future change in annual mean surface PM₂.₅, of up to 12µg m⁻³, shows fairly good agreement between three CMIP6 models (UKESM1-0-LL, GFDL-ESM4 and CESM2-WACCM) as projections in 2050 and 2095 are all within the range of each of the individual models. The future increases in annual mean surface PM₂.₅ appear to be strongly driven by emission changes as there are strong positive correlations between these variables across South Asia in all models (Fig. 16). Across South Asia, all models simulate a larger increase in DJF mean surface PM₂.₅ concentrations, of up to 18µg m⁻³ by 2050, than what occurs in JJA, reflecting the seasonality shown in the model evaluation. The MIROC-ES2L model predicts smaller future increases in surface PM₂.₅, of up to 5µg m⁻³, than the other models across South Asia in both 2050 and 2095. This is a result of smaller changes in the BC,
<figure><img src="image_32.png" /><figcaption>**Figure 14.** Future global and regional changes in annual mean surface PM<sub>2.5</sub>, relative to 2005–2014 mean, for the different SSPs used in CMIP6. Each line represents a multi-model mean across the region, with shading representing the ±1 SD of the mean. See Table 1 for details of models contributing to each scenario. The multi-model regional mean value (±1 SD) for the years 2005–2014 is shown in the top left corner of each panel.</figcaption></figure>

OA and sulphate aerosol components in the MIROC-ES2L model despite increases in aerosols and aerosol precursor emissions across South Asia in ssp370 (Fig. S24–S26).

Disagreements in both the sign and magnitude of simulated future annual and seasonal mean surface PM<sub>2.5</sub> changes between CMIP6 models are also exhibited across East Asia. Small regional annual mean increases are predicted in 2050 due to PM<sub>2.5</sub> increases in JJA from all models apart from GFDL-ESM4. A larger reduction in the SO<sub>4</sub> component is simulated over East Asia by GFDL-ESM4 than in other models (Fig. S25), resulting in an overall decrease in PM<sub>2.5</sub>. In 2095 most models simulate a reduction in PM<sub>2.5</sub> concentrations in both seasons across East Asia, apart from CESM2-WACCM due to the increase in JJA. All models simulate continual reductions out to 2100 for SO<sub>4</sub> across this region, whereas BC increases in the near term before decreasing out to 2100. For OA, CESM2-WACCM shows larger increases over East Asia in both 2050 and 2095 compared to the other

models, which show a smaller increase in 2050 and a reduction by 2095 (Fig. S26). CESM2-WACCM includes a more complex treatment of SOA formation, showing a strong response to climate and historical trends in OA (Tilmes et al., 2019). Positive correlations are shown for CESM2-WACCM between surface PM<sub>2.5</sub> and emissions of BVOC as well as between surface PM<sub>2.5</sub> and temperature (Fig. 16), which are not present in other models and could explain the differences between this model and others across East Asia. The discrepancies in CMIP6 models are not as obvious over South Asia as the effect of the increase in OA over South Asia in CESM2-WACCM is masked by coincident increases in other components across other models, as indicated by the strong correlations with emissions here. CESM2-WACCM also shows larger simulated increases in PM<sub>2.5</sub> over South America, Central America, Southern Africa and South East Asia than other models, which can be attributed to the larger increase in the OA fraction (Fig. S26) and the strong corre-
lations in this model with changes in temperature and emissions (BVOCs and SO₂). Over Southern Africa UKESM1-0-LL shows a reduction in future PM₂.₅ in contrast to other models due to a reduction in the BC, OA and dust aerosol components (Figs. S24, S26 and S27). UKESM1-0-LL exhibits particularly strong negative correlations for surface PM₂.₅ when compared with temperature and precipitation. These relationships over Southern Africa are quite different to other CMIP6 models, which is also highlighted in the model evaluation over this region (Fig. 8) and indicates that climate change influences aerosol concentrations differently over this region in this model (Fig. 16). In addition, there is a slight positive correlation of PM₂.₅ with BVOC emissions in UKESM1-0-LL over Southern Africa. Future biogenic emissions (including monoterpenes) reduce here in ssp370 (Fig. S22), potentially due to land-use vegetation change as UKESM1-0-LL has dynamic vegetation coupled to BVOC emissions (Table S1 in the Supplement). This could also reduce PM₂.₅ concentrations over this region because monoterpene emissions are the main precursor to SOA formation in UKESM1-0-LL (Mulcahy et al., 2020).

The annual and seasonal mean PM₂.₅ response is variable across individual CMIP6 models over regions close to natural sources of particulate matter (North Africa; Central Asia; and Pacific, Australia and New Zealand). Over these regions there is a large range in both the sign and magnitude of the annual and seasonal PM₂.₅ response, which can be mainly attributed to the dust fraction (Fig. S27) and the fact that this aerosol source has a large inter-annual variability in its emission strength. There is also a lack of consistency across CMIP6 models in the correlations of PM₂.₅ with any individual driver, indicating the variability of aerosol sources in these regions within models. Interestingly, the CMIP6 models do not agree in the sign and magnitude of future changes to dust concentrations in ssp370 (Fig. S27).

Across the ocean and North Pole regions, all the CMIP6 models tend to simulate a small increase in PM₂.₅ concentrations, which can be attributed to increases in sea salt concentrations (Fig. S28). A strong increase in sea salt concentrations is simulated in all models across the Southern Ocean (and other oceans), potentially driven by changes to meteorological conditions (reflected by the positive correlations of PM₂.₅ with the climate variables temperature and precipitation in Fig. 16), which increase wind speed and sea salt emissions. As ssp370 is a scenario with a large climate change signal, the increases in PM₂.₅ across the North Pole, particularly in 2100, can be attributed to the melting of sea ice increasing sea salt emissions, which again is reflected in the positive correlations of PM₂.₅ with climate variables over this region. However, the magnitude of this response is different in the CMIP6 models due to the underlying ECS and the response of Arctic surface temperatures within the individual model.

The differences in the simulated future PM₂.₅ changes across the CMIP6 models in ssp370 highlight that it is im-

portant to consider how natural sources of aerosol respond in a future climate in addition to changes from anthropogenic emissions. Particular differences between models have been shown for dust, sea salt and also organic (secondary) aerosols, which should be explored further. In addition, the different representations of aerosols within individual models, e.g. organic aerosols, are an important consideration as they can make a large difference to any future regional projection of PM₂.₅.

## 6 Conclusions

In this study we have provided an initial analysis of the historical and future changes in air pollutants (O₃ and PM₂.₅) from the latest generation of Earth system and climate models that have submitted results from experiments conducted as part of CMIP6. Data were available from the historical experiments of 6 CMIP6 models for surface O₃ and 11 models for surface PM₂.₅. Historical changes in regional concentrations of O₃ and PM₂.₅ are presented over the period 1850 to 2014 using data from all models. A present-day model evaluation of the CMIP6 models was conducted against surface observations of O₃ and PM₂.₅ obtained from the TOAR and GASSP databases, respectively. An additional comparison was performed for simulated PM₂.₅ concentrations against the MERRA-2 aerosol reanalysis product. An assessment is then made of the changes in surface O₃ and PM₂.₅ simulated by the CMIP6 models across different future scenarios, ranging from weak to strong air pollutant and climate mitigation.

The six CMIP6 models simulate present-day (2005–2014) surface O₃ concentrations that are elevated in the Northern Hemisphere summer, with lower values throughout the year across the Southern Hemisphere. However, a large model diversity is shown across the continental Northern Hemisphere due to the large simulated seasonal cycles in certain models. Compared to surface O₃ measurements, CMIP6 models overestimate observed annual mean values and in both summer and winter across most regions by up to 16 ppb (a similar result to previous multi-model evaluations of global chemistry–climate models in Young et al., 2018). An exception to this is at observation locations across Antarctica, where CMIP6 models tend to underestimate observed values by 5 ppb.

Large surface PM₂.₅ concentrations are simulated in CMIP6 models near dust and anthropogenic-emission source regions. Model diversity across the CMIP6 models is largest near the dust source regions due to their sensitivity to meteorological variability, whereas across other regions the CMIP6 models are relatively similar in their simulation of PM₂.₅ concentrations. Evaluating the approximate PM₂.₅ calculated from CMIP6 models (excluding nitrate aerosols) against ground-based PM₂.₅ observations shows an underestimation across most regions of up to 10 µg m⁻³. The underestimation of observations by models is larger in the Northern Hemi-
<figure><img src="image_33.png" /><figcaption>**Figure 15.** Future global and regional changes in the annual and seasonal mean surface PM<sub>2.5</sub>, relative to the 2005–2014 mean, for the ssp370 pathway used in CMIP6. Each black circle represents the annual mean response for an individual model in (a) 2045–2055 and (b) 2090–2100, with the coloured bars showing the SD across the annual mean. The DJF and JJA seasonal mean responses averaged over the relevant 10-year periods are shown by squares and triangles, respectively. The multi-model regional mean over the period 2005–2014 is given towards the left of each panel.</figcaption></figure>

sphere winter than summer, in part due to the absence of nitrate aerosols within most CMIP6 models and also due to underrepresentation of other aerosol processes within global models (a similar result to other multi-model assessments). To improve the spatial coverage and consistency of the PM<sub>2.5</sub> evaluation with CMIP6 models, an additional comparison was made to the MERRA-2 aerosol reanalysis product. A similar but slightly smaller underestimation of PM<sub>2.5</sub> concentrations over Europe and North America was found in the comparison of CMIP6 models and MERRA-2, providing further confidence in the result from the ground-based comparison. CMIP6 models overestimated the monthly PM<sub>2.5</sub> concentrations in MERRA-2 over South and East Asia by up to 15 µg m<sup>-3</sup>, in contrast to the evaluation using ground-based observations. Mean annual cycles simulated by CMIP6 models and MERRA-2 tend to agree across other regions for which there are no suitable ground-based observations. The comparison of surface O<sub>3</sub> and PM<sub>2.5</sub> simulated by CMIP6 models to observations shows similar biases to previous generations of global composition–climate models. Further studies are required (e.g. global sensitivity or process studies) to

explore uncertainties in models and the differences with observations.

Across the historical period (1850–2014), the CMIP6 models simulated a global annual increase in surface O<sub>3</sub> of between 7 and 14 ppb, with a larger increase in JJA than DJF. A global multi-model mean increase of 11.7 ± 2.3 ppb was simulated by the CMIP6 models, which agrees well with the change previously simulated by CMIP5 models. A large diversity in the historical change in surface O<sub>3</sub> was simulated by CMIP6 models across South Asia and other Northern Hemisphere regions. CMIP6 models predicted larger historical changes in surface O<sub>3</sub> than those from an emission-only-driven parameterisation, indicating a potential climate change impact (Wu et al., 2008; Bloomer et al., 2009; Weaver et al., 2009; Rasmussen et al., 2013; Colette et al., 2015) on surface O<sub>3</sub> over the historical period. Small global increases in surface PM<sub>2.5</sub> are simulated over the historical period by CMIP6 models, with larger regional changes of up to 12 µg m<sup>-3</sup> on an annual mean basis and up to 18 µg m<sup>-3</sup> in DJF across East and South Asia. The largest diversity in the response of CMIP6 models occurs over Asian regions, with large inter-annual variabilities near dust source regions.
<figure><img src="image_34.png" /><figcaption>**Figure 16.** Correlation coefficients calculated when comparing future annual mean surface PM<sub>2.5</sub> concentrations against individual variables from individual CMIP6 models (that had data out to 2100) over the period 2015 to 2100 in the ssp370 scenario: precipitation; surface air temperature (TAS); emissions of biogenic volatile organic compounds (BVOCs); and emissions of SO<sub>2</sub>, black carbon (BC) and organic carbon (OC).</figcaption></figure>

CMIP6 models simulate the peak in PM<sub>2.5</sub> concentrations in the 1980s across Europe and North America prior to simulating the observed decline in concentrations to present day (Leibensperger et al., 2012; Tørseth et al., 2012; Turnock et al., 2015), attributed to the implementation of air pollutant emission controls over these regions.

The CMIP6 models predict that surface O<sub>3</sub> will increase across most regions in the weak-mitigation scenarios (ssp370 and ssp585), particularly over South and East Asia (up to 16 ppb by 2100) due to a combination of increases in air pollutant emissions, increases in global CH<sub>4</sub> abundances and climate change. Discrepancies exist in the regional surface O<sub>3</sub> response in ssp370 between individual CMIP6 models due to differences in the response of chemistry (NO<sub>x</sub>; Fig. S17), climate (temperature) and biogenic precursor emissions (Fig. S22). Benefits to regional air quality from large reductions in surface O<sub>3</sub> are possible across all regions

for scenarios that contain strong climate and air pollutant mitigation measures, including those targeting CH<sub>4</sub>.

CMIP6 models predict that surface PM<sub>2.5</sub> concentrations will decrease across all regions in both the middle-of-the-road (ssp245) and strong-mitigation scenarios (ssp126) by up to 12 µg m<sup>-3</sup> due to the reduction in anthropogenic aerosols and aerosol precursor emissions, yielding a benefit to regional air quality, whereas for the weak climate and air pollutant mitigation scenario (ssp370), annual and seasonal mean surface PM<sub>2.5</sub> is simulated to increase across a number of regions. Implementing mitigation measures specifically targeting SLCFs on top of the ssp370 scenario shows immediate improvements in PM<sub>2.5</sub> concentrations, restricting any changes to below present-day values. The largest change in regional mean PM<sub>2.5</sub> concentrations and also largest diversity across CMIP6 models are predicted in ssp370 across South Asia, an area with already poor air quality. Disagreements in the projection of future changes to regional sur-
face PM$_{2.5}$ concentrations between individual CMIP6 models can be attributed to differences in the complexity of the aerosol schemes implemented within models, in particular the formation mechanisms of organic aerosols and emission of BVOCs over certain regions, along with the strength of the climate change signal (temperature and precipitation) simulated by models and the impact this has on natural aerosol emissions via Earth system couplings.

The results from CMIP6 provide an opportunity to assess the simulation of historical and future changes in air pollutants within the latest generation of Earth system and climate models using up-to-date scenarios of future socioeconomic development. Large changes in air pollutants were simulated over the historical period, primarily in response to changes in anthropogenic emissions. Future regional concentrations of air pollutants depend on the particular trajectory of climate and air pollutant mitigation that the world follows, with important consequences for regional air quality and human health. Substantial benefits can be achieved across most world regions by implementing measures to mitigate the extent of climate change as well as from large reductions in air pollutant emissions, including CH$_{4}$, which is particularly important for controlling O$_{3}$. In future scenarios which do not mitigate climate change and air pollutant emissions, the regional concentrations of air pollutants are anticipated to increase. Important differences between individual CMIP6 models have been identified in terms of how they simulate air pollutants from the interaction of chemistry (O$_{3}$ and NO$_{x}$), climate (temperature and precipitation) and natural precursor emissions (BVOCs) in the future. Further research and understanding of these processes are necessary to improve the robustness of regional projections of air pollutants on climate change timescales (decadal to centennial).

***Data availability.*** CMIP6 data are archived at the Earth System Grid Federation and are freely available to download. The data on ESGF can be accessed via the website interface https://pcmdi.llnl.gov/CMIP6/ (last access: 8 September 2020, WCRP, 2020), and all the relevant references to the data used are provided in Table 1 as stated in the text.

***Supplement.*** The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-14547-2020-supplement.

***Author contributions.*** STT conducted the analysis and wrote the paper with contributions from RJA, TW and JZ performed BCC-ESM1 simulations. LE and ST performed CESM2-WACCM simulations. PN and MM performed CNRM-ESM2-1 simulations. LH and JGJ performed GFDL-ESM4 simulations. SEB and KT performed GISS-E2-1-G simulations. MA and PG performed HadGEM3-GC31-LL simulations. TT performed MIROC6-ES2L simulations. DN performed MPI-ESM1.2-HAM simulations. MD and NO performed MRI-ESM2-0 simulations. DO and MS performed

NorESM2-LM simulations. AS, FMO’C and SS performed UKESM1-0-LL simulations. All co-authors have been involved in providing comments and editing the manuscript.

***Competing interests.*** The authors declare that they have no conflict of interest.

***Special issue statement.*** This article is part of the special issue “The Aerosol Chemistry Model Intercomparison Project (AerChemMIP)”. It is not associated with a conference.

***Acknowledgements.*** For making their measurement data available to be used in this study, we would like to acknowledge the providers who supplied their data to the GASSP database and TOAR database.

Kostas Tsigaridis and Susanne E. Bauer acknowledge resources supporting this work provided by the NASA High-End Computing (HEC) programme through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center.

***Financial support.*** Steven T. Turnock and Fiona M. O’Connor have been supported by the BEIS and DEFRA Met Office Hadley Centre Climate Programme (GA01101). Steven T. Turnock has been supported by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. Fiona M. O’Connor has been supported by the EU Horizon 2020 research programme (CRESCENDO (grant agreement no. 641816)). Toshihiko Takemura has been supported by the supercomputer system of the National Institute for Environmental Studies, Japan, and JSPS KAKENHI (grant no. JP19H05669). Makoto Deushi and Naga Oshima have been supported by the Japan Society for the Promotion of Science (grant nos. JP18H03363, JP18H05292 and JP20K04070) and the Environmental Restoration and Conservation Agency of Japan through the Environment Research and Technology Development Fund (grant nos. JPMEERF20172003, JPMEERF20202003 and JPMMEERF20205001). David Neubauer has been supported by the European Union’s Horizon 2020 research and innovation programme project (FORCeS (grant agreement no. 821205)) and Deutsches Klimarechenzentrum (DKRZ; project ID 1051). Sungbo Shim has been supported by the Korea Meteorological Administration research and development programme “Development and Assessment of IPCC AR6 Climate change scenario” (grant no. KMA2018-00321).

***Review statement.*** This paper was edited by Holger Tost and reviewed by two anonymous referees.

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