Editing IPCC:AR6/WGI/Chapter-6 (section)

=== 6.3.5 Aerosols ===

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This section assesses trends in the atmospheric distribution of aerosols and improvements in relevant physical and chemical processes. The observed large-scale temporal evolution of aerosols is assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.2.6|Section 2.2.6]] . Since AR5, long-term measurements of aerosol mass concentrations from regional global surface networks have continued to expand and provide information on the distribution and trends in aerosols (Figure 6.7). There is large spatial variability in aerosol mass concentration, expressed as PM <sub>2.5</sub> , dominant aerosol type and aerosol composition, consistent with the findings in AR5.

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[[File:179ed8251722ef95f81759ee7c0bd069 IPCC_AR6_WGI_Figure_6_7.png]]

'''Figure 6.7 |''' '''Distribution of PM''' <sub>2.5</sub> <sub></sub> '''composition mass concentration (in μg m''' <sup>–3</sup> ''') for the major PM''' <sub>2.5</sub> '''aerosol components.''' Those aerosol components are sulphate, nitrate, ammonium, sodium, chloride, organic carbon and elemental carbon. The central world map depicts the intermediate-level regional breakdown of observations (10 regions) following the IPCC Sixth Assessment Report Working Group III (AR6 WGIII). Monthly averaged PM <sub>2.5</sub> aerosol component measurements are from: '''''(i)''''' the Environmental Protection Agency (EPA) network which include 211 monitor sites primarily in urban areas of North America during 2000–2018 ( [[#Solomon--2014|Solomon et al., 2014]] ), '''''(ii)''''' the Interagency Monitoring of Protected Visual Environments (IMPROVE) network during 2000–2018 over 198 monitoring sites representative of the regional haze conditions over North America, '''''(iii)''''' the European Monitoring and Evaluation Programme (EMEP) network over 70 monitoring in Europe and (eastern) Eurasia during 2000–2018, '''''(iv)''''' the Acid Deposition Monitoring Network in Eastern Asia (EANET) network with 39 (18 remote, 10 rural, 11 urban) sites in Eurasia, Eastern Asia, South East Asia and Developing Pacific, and Asia-Pacific Developed during 2001–2017, '''''(v)''''' the global Surface Particulate Matter Network (SPARTAN) during 2013–2019 with sites primarily in highly populated regions around the world (i.e., North America, Latin America and Caribbean, Africa, Middle East, Southern Asia, Eastern Asia, South East Asia and Developing Pacific; [[#Snider--2015|Snider et al., 2015]] , 2016), and '''''(vii)''''' individual observational field campaign averages over Latin America and Caribbean, Africa, Europe, Eastern Asia, and Asia-Pacific Developed ( [[#Celis--2004|Celis et al., 2004]] ; [[#Feng--2006|Feng et al., 2006]] ; [[#Bourotte--2007|Bourotte et al., 2007]] ; [[#Fuzzi--2007|Fuzzi et al., 2007]] ; [[#Mariani--2007|Mariani and de Mello, 2007]] ; [[#Molina--2007|Molina et al., 2007]] , 2010; [[#Favez--2008|Favez et al., 2008]] ; [[#Mkoma--2008|Mkoma, 2008]] ; [[#Aggarwal--2009|Aggarwal and Kawamura, 2009]] ; [[#Mkoma--2009|Mkoma et al., 2009]] ; [[#de%20Souza--2010|de Souza et al., 2010]] ; [[#Li--2010|Li et al., 2010]] ; [[#Martin--2010|Martin et al., 2010]] ; [[#Radhi--2010|Radhi et al., 2010]] ; [[#Weinstein--2010|Weinstein et al., 2010]] ; [[#Batmunkh--2011|Batmunkh et al., 2011]] ; [[#Gioda--2011|Gioda et al., 2011]] ; [[#Pathak--2011|Pathak et al., 2011]] ; F. [[#Zhang--2012|]] [[#Zhang--2012|Zhang et al., 2012]] ; [[#Cho--2013|Cho and Park, 2013]] ; [[#Zhao--2013|Zhao et al., 2013]] ; [[#Wang--2019|Wang et al., 2019]] ; [[#Kuzu--2020|Kuzu et al., 2020]] ). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

Remote-sensing instruments provide a larger-scale view of aerosol distributions and trends than ground-based monitoring networks by retrieving the aerosol optical depth (AOD), which is indirectly related to aerosol mass concentrations. AOD is the column-integrated measure of extinction of the solar intensity due to aerosols at a given wavelength, and is therefore relevant to the estimation of the radiative forcing of aerosol–radiation interactions ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.3.1|Section 7.3.3.1]] ). Models participating in Phase III of the AeroCom intercomparison project were found to underestimate present-day AOD by about 20% ( [[#Gliß--2021|Gliß et al., 2021]] ), although different remote-sensing estimates obtain different estimates of global mean AOD. [[#Gliß--2021|Gliß et al. (2021)]] also highlight the considerable diversity in the simulated contribution of various aerosol types to total AOD. However, models simulate regional trends in AODs that agree well, when expressed as percentage change, with ground- (Mortier et al. , 2020; Gliß et al. , 2021) and satellite-based ( [[#Cherian--2020|Cherian and Quaas, 2020]] ; [[#Gliß--2021|Gliß et al., 2021]] ) observations. AOD trends simulated by CMIP6 models are more consistent with satellite-derived trends than CMIP5 models for several sub-regions, thanks to improved emissions estimates ( [[#Cherian--2020|Cherian and Quaas, 2020]] ).

All CMIP6 models simulate a positive trend in global mean AOD from 1850, with a strong increase after the 1950s coinciding with the massive increase in anthropogenic SO <sub>2</sub> emissions (Figure 6.8). Global mean AOD increases have slowed since 1980, or even reversed in some models, as a result of a compensation between SO <sub>2</sub> emissions decreases over the USA and Europe in response to air-quality controls since the mid-1980s, and increases over Asia. From about 2000, global mean AOD stabilized in the models, driven by soaring emissions in Southern Asia and declining emissions in Eastern Asia (Section 6.2.1). Trends after around 2010 are difficult to assess from CMIP6 models because the historical simulations end at 2014. Nevertheless, the strong decline in anthropogenic SO <sub>2</sub> emissions over Eastern Asia since 2011 is underestimated in the CMIP6 emissions database ( [[#Hoesly--2018|Hoesly et al., 2018]] ), indicating that the observed AOD change over Eastern Asia may not be captured accurately by CMIP6 models ( [[#Wang--2021|Wang et al., 2021]] ). While all CMIP6 models simulate the increase of AOD between 1850 and 2014 there is strong inter-model diversity in the simulated AOD change since 1850 ranging from 0.01 (15%) to 0.08 (53%) in 2014. Some models therefore lie outside the 68% confidence interval of 0.02 (15%) to 0.04 (or 30%) for global AOD change in 2005–2015 compared to 1850, estimated by [[#Bellouin--2020|Bellouin et al. (2020)]] based on observational and model (excluding CMIP6) lines of evidence. In addition to the horizontal distribution of aerosols documented by AOD, their number size distribution, vertical distribution, optical properties, hygroscopicity, ability to act as CCN, chemical composition, mixing state and morphology are key elements to assess their climate effect (Section 6.4).

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[[File:2de691d569e4677992259ed244abf7c1 IPCC_AR6_WGI_Figure_6_8.png]]

'''Figure 6.8 |''' '''Time evolution of changes in global mean aerosol optical depth (AOD) at 550 nm.''' The year of reference is 1850. Data are shown from individual Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations. Each time series corresponds to the ensemble mean of realizations done by each model. Simulation results from years including major volcanic eruptions (e.g., Novarupta, 1912; Pinatubo, 1991), are excluded from the analysis for models encompassing the contribution of stratospheric volcanic aerosols to total AOD. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

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==== 6.3.5.1 Sulphate (SO <sub>4</sub> <sup>2–</sup> ) ====

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Sulphate aerosols (or sulphate-containing aerosols) are emitted directly or formed in the atmosphere by gas- and aqueous-phase oxidation of precursor sulphur gases, including SO <sub>2</sub> , DMS and carbonyl sulphide (OCS), emitted from anthropogenic and natural sources (Section 6.2). Sulphate aerosols influence climate forcing directly by either scattering solar radiation or absorbing longwave radiation, and indirectly by influencing cloud micro- and macrophysical properties and precipitation ( [[#Boucher--2013|Boucher et al., 2013]] ; [[#Myhre--2013|Myhre et al., 2013]] ). Additionally, sulphate aerosols and sulphate deposition have a large impact on air quality and ecosystems ( [[#Reis--2012|Reis et al., 2012]] ). The majority of sulphate particles are formed in the troposphere, however, SO <sub>2</sub> and other longer-lived natural precursors, such as OCS, transported into the stratosphere, contribute to the background stratospheric aerosol layer ( [[#Kremser--2016|Kremser et al., 2016]] ). SO <sub>2</sub> emissions from volcanic eruptions are a significant source of stratospheric sulphate loading (see [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] for reconstruction of stratospheric aerosol optical depth and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] for radiative forcing of volcanic aerosols). Furthermore, studies suggest sulphate contributions from anthropogenic SO <sub>2</sub> emissions transported into the stratosphere could have a consequent impact on radiative forcing ( [[#Myhre--2004|Myhre et al., 2004]] ; [[#Yu--2016|Yu et al., 2016]] ). However, there is significant uncertainty in the relative importance of this stratospheric sulphate source ( [[#Kremser--2016|Kremser et al., 2016]] ).

Process understanding of sulphate production pathways from SO <sub>2</sub> emissions has seen some progress since AR5. More specifically, many global climate models now have a more complete description of chemical reactions such that oxidant levels (including ozone) are better described, include a pH-dependence of SO <sub>2</sub> oxidation (e.g., [[#Kirkevåg--2018|Kirkevåg et al., 2018]] ; [[#Bauer--2020|Bauer et al., 2020]] ) , and implement explicit descriptions of ammonium and nitrate aerosol components, which may influence the partitioning of sulphate (Bian et al. , 2017; Lund et al. , 2018a) . The pH influences the heterogeneous chemistry as well as the physical properties of the aerosols, and this topic has been a subject of growing interest since AR5 ( [[#Cheng--2016|Cheng et al., 2016]] ; [[#Freedman--2019|Freedman et al., 2019]] ; [[#Nenes--2020|Nenes et al., 2020]] ). Increases in cloudwater pH have been shown to significantly increase the radiative forcing due to sulphate aerosols ( [[#Turnock--2019|Turnock et al., 2019]] ).

Sulphate is removed from the atmosphere by dry deposition and wet scavenging, and these processes depend on the characteristics of the Earth’s surface, and the intensity, frequency and amount of precipitation ( [[#Boucher--2013|Boucher et al., 2013]] ). Even though there have been some improvements since AR5, representation of atmospheric transport and of wet scavenging and related cloud processes remains a key source of uncertainty in the simulated aerosol distribution and lifetime, with further consequences for the sulphate forcing estimates ( [[#Kristiansen--2016|Kristiansen et al., 2016]] ; [[#Lund--2018a|Lund et al., 2018a]] ). There are also still relatively large uncertainties in the emission height used in models affecting the simulated aerosol distribution (Yang et al. , 2019a) .

Based on long-term surface-based in situ observations, AR5 reported a strong decline in sulphate aerosols in Europe and the USA over 1990–2009, with the largest decreases occurring before 2000 in Europe and post-2000 in the USA. Since AR5, atmospheric measurements in conjunction with model results have provided insights into the spatial and temporal distribution of sulphate and sulphur deposition ( [[#Vet--2014|Vet et al., 2014]] ; [[#Tan--2018|Tan et al., 2018]] ; [[#Aas--2019|Aas et al., 2019]] ). The in situ observations in North America and Europe reveal substantial reduction since the measurements started around 1980, though the trends have not been linear through this period (Table 6.5). Several regional studies agree with these trend estimates for Europe (Banzhaf et al. , 2015; Theobald et al. , 2019) and North America ( [[#Sickles%20II--2015|Sickles II and Shadwick, 2015]] ; Paulot et al. , 2016) . Further, the concentrations of primary emitted SO <sub>2</sub> (Section 6.3.3.5) show greater decreases than secondary sulphate aerosols over these regions due to a combination of higher oxidation rate (hence more SO <sub>2</sub> converted to SO <sub>4</sub> <sup>2–</sup> ) and increased dry deposition rate of SO <sub>2</sub> (Fowler et al. , 2009; Banzhaf et al. , 2015) . In situ observations over other parts of the world are scattered (Figure 6.7), and the lack of observations makes it too uncertain to quantify regional representative trends ( [[#Hammer--2018|Hammer et al., 2018]] ). However, limited in situ observations in Eastern Asia indicate an increase in atmospheric sulphate up to around 2005 and then a decline ( [[#Aas--2019|Aas et al., 2019]] ), which is confirmed by satellite observations of SO <sub>2</sub> (Section 6.3.3.5). In India, on the other hand, satellite observations indicate a rapid increase in the SO <sub>2</sub> levels ( [[#Krotkov--2016|Krotkov et al., 2016]] ), and long-term measurements of sulphate in precipitation in India further provide evidence of an increasing trend from 1980–2010 ( [[#Bhaskar--2017|Bhaskar and Rao, 2017]] ; [[#Aas--2019|Aas et al., 2019]] ). Further improvements in global trend assessments are expected with new integrated reanalysis products from the Earth-system data assimilation projects ( [[#Randles--2017|Randles et al., 2017]] ; [[#Inness--2019|Inness et al., 2019]] ).

Indirect evidence of decadal trends in the atmospheric loading of sulphur are provided by Alpine ice cores, mainly influenced by European sources ( [[#Engardt--2017|Engardt et al., 2017]] ), and ice cores from Svalbard ( [[#Samyn--2012|Samyn et al., 2012]] ) and Greenland ( [[#Patris--2002|Patris et al., 2002]] ; [[#Iizuka--2018|Iizuka et al., 2018]] ) influenced by sources in Europe and North America. These show similar patterns with a weak increase from the end of the 19th century up to around 1950, followed by a steep increase up to around 1980, and then a significant decrease over the next two decades (see also [[IPCC:Wg1:Chapter:Chapter-2#2.2.6|Section 2.2.6]] ). This general trend is consistent with the emissions of SO <sub>2</sub> in North America and Europe (Figures 6.18 and 6.19; Hoesly et al. , 2018) .

Global and regional models qualitatively reproduce observed trends over North America and Europe for the period 1990–2015 for which emissions changes are generally well quantified ( [[#Aas--2019|Aas et al., 2019]] ; [[#Mortier--2020|Mortier et al., 2020]] ), building confidence in the relationship between emissions, concentration, deposition and radiative forcing derived from these models. However, the models seem to systematically underestimate sulphate ( [[#Bian--2017|Bian et al., 2017]] ; [[#Lund--2018a|Lund et al., 2018a]] ) and AOD ( [[#Lund--2018a|Lund et al., 2018a]] ; [[#Gliß--2021|Gliß et al., 2021]] ), and there are quite large differences in the models’ distribution of the concentration fields of sulphate driven by differences in the representation of photochemical production and sinks of aerosols. One global model study also highlighted biases in simulated sulphate trends over the 2001–2015 period over eastern China due to uncertainties in the CEDS anthropogenic SO <sub>2</sub> emissions trends (Paulot et al. , 2018a) .

In summary, there is ''high confidence'' that the global tropospheric sulphate burden increased from 1850 to around 2005, but there are large regional differences in the magnitude. Sulphate aerosol concentrations in North America and Europe have declined over 1980–2015 with slightly stronger reductions in North America (47 ± 20%) than in Europe (40 ± 30%) over 2000–2015, though Europe had larger reductions in the prior decade (1990–2000; 52 ± 21% and 21 ± 14% respectively for Europe and North America). In Asia, the trends are more scattered, though there is ''medium confidence'' that there was a strong increase up to around 2005, followed by a steep decline in China, while over India, the concentrations are increasing steadily.

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==== 6.3.5.2 Ammonium (NH <sub>4</sub> <sup>+</sup> ) and Nitrate Aerosols (NO <sub>3</sub> <sup>–</sup> ) ====

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Ammonium sulphate and ammonium nitrate aerosols are formed when NH <sub>3</sub> reacts with nitric acid (HNO <sub>3</sub> ) and sulphuric acid (H <sub>2</sub> SO <sub>4</sub> ), produced in the atmosphere by the oxidation of NO <sub>x</sub> and SO <sub>2</sub> respectively. Ammonium nitrate is formed only after H <sub>2</sub> SO <sub>4</sub> is fully neutralized. NH <sub>4</sub> <sup>+</sup> and NO <sub>3</sub> <sup>–</sup> aerosols produced via these gas-to-particle reactions are a major fraction of fine-mode particles (with diameter &lt;1µm) affecting air quality and climate. Coarse-mode nitrate, formed by the heterogeneous reaction of nitric acid with dust and sea salt, dominates the overall global nitrate burden, but has little radiative effect ( [[#Hauglustaine--2014|Hauglustaine et al., 2014]] ; [[#Bian--2017|Bian et al., 2017]] ). Trends in ammonium (NH <sub>4</sub> <sup>+</sup> ) and nitrate (NO <sub>3</sub> <sup>–</sup> ) were not assessed in AR5.

Global model present-day estimates of the global NH <sub>4</sub> <sup>+</sup> burden range from 0.1–0.6 TgN ( [[#Bian--2017|Bian et al., 2017]] ). Models generally simulate surface NH <sub>4</sub> <sup>+</sup> concentrations better than surface NH <sub>3</sub> concentrations ( [[#Bian--2017|Bian et al., 2017]] ), which reflects its thermodynamic control by SO <sub>4</sub> <sup>2–</sup> rather than NH <sub>3</sub> ( [[#Shi--2017|Shi et al., 2017]] ). The concomitant increases of NH <sub>3</sub> , SO <sub>2</sub> , and NO <sub>x</sub> emissions (see Section 6.2) have led to a factor of three to nine increase in the simulated NH <sub>4</sub> <sup>+</sup> burden from 1850–2000 ( [[#Hauglustaine--2014|Hauglustaine et al., 2014]] ; [[#Lund--2018a|Lund et al., 2018a]] ), driven primarily by ammonium sulphate (70–90%). The increases in the NH <sub>3</sub> and NH <sub>4</sub> <sup>+</sup> burdens are indirectly supported by the observed increase of NH <sub>4</sub> <sup>+</sup> concentration in ice cores in mid- to high latitudes (Kang et al. , 2002; Kekonen et al. , 2005; Lamarque et al. , 2013; Iizuka et al. , 2018) .

Ammonium nitrate is semi-volatile, which results in complex spatial and temporal patterns in its concentrations ( [[#Putaud--2010|Putaud et al., 2010]] ; [[#Hand--2012|Hand et al., 2012]] a; H. [[#Zhang--2012|]] [[#Zhang--2012|Zhang et al., 2012]] ), reflecting variations in its precursors, NH <sub>3</sub> and HNO <sub>3</sub> , as well as SO <sub>4</sub> <sup>2–</sup> , non-volatile cations, temperature and relative humidity (Nenes et al. , 2020) . High relative humidity and low temperature as well as elevated fine particulate matter loading (Huang et al. , 2014; Petit et al. , 2015; H. Li et al. , 2016; Sandrini et al. , 2016) favour nitrate production. Measurements reveal the high contribution of NO <sub>3</sub> <sup>–</sup> to surface PM <sub>2.5</sub> (&gt;30%) in regions with elevated regional NO <sub>x</sub> and NH <sub>3</sub> emissions, such as the Paris area ( [[#Beekmann--2015|Beekmann et al., 2015]] ; [[#Zhang--2019|Zhang et al., 2019]] ), northern Italy ( [[#Masiol--2015|Masiol et al., 2015]] ; [[#Ricciardelli--2017|Ricciardelli et al., 2017]] ), Salt Lake City ( [[#Kuprov--2014|Kuprov et al., 2014]] ; [[#Franchin--2018|Franchin et al., 2018]] ), the North China Plains ( [[#Guo--2014|Guo et al., 2014]] ; [[#Chen--2016|Chen et al., 2016]] ) and New Delhi ( [[#Pant--2015|Pant et al., 2015]] ). Recent observations also show that ammonium nitrate contributes to the Asian tropopause aerosol layer ( [[#Vernier--2018|Vernier et al., 2018]] ; [[#Höpfner--2019|Höpfner et al., 2019]] ). Model diversity in simulating the present-day global fine-mode NO <sub>3</sub> <sup>–</sup> burden is large with two multi-model intercomparison studies reporting estimates in the range of 0.14–1.88 Tg and 0.08–0.93 Tg respectively ( [[#Bian--2017|Bian et al., 2017]] ; [[#Gliß--2021|Gliß et al., 2021]] ). Models differ in their estimates of the global tropospheric nitrate burden by up to a factor of 13 with differences remaining nearly the same across the CMIP5 and CMIP6 generation of models ( [[#Bian--2017|Bian et al., 2017]] ; [[#Gliß--2021|Gliß et al., 2021]] ). While regional patterns in the concentration of fine-mode NO <sub>3</sub> <sup>–</sup> are qualitatively captured by models, the simulation of fine-mode NO <sub>3</sub> <sup>–</sup> is generally worse than that of NH <sub>4</sub> <sup>+</sup> or SO <sub>4</sub> <sup>2–</sup> ( [[#Bian--2017|Bian et al., 2017]] ). This can be partly attributed to the semi-volatile nature of ammonium nitrate and biases in the simulation of its precursors ( [[#Heald--2014|Heald et al., 2014]] ; [[#Paulot--2016|Paulot et al., 2016]] ), including the sub-grid scale heterogeneity in NO <sub>x</sub> and NH <sub>3</sub> emissions ( [[#Zakoura--2018|Zakoura and Pandis, 2018]] ).

Models indicate that the burden of fine-mode NO <sub>3</sub> <sup>–</sup> has increased by a factor of two to five from 1850–2000 ( [[#Xu--2012|Xu and Penner, 2012]] ; [[#Hauglustaine--2014|Hauglustaine et al., 2014]] ; [[#Lund--2018a|Lund et al., 2018a]] ), an increase that has accelerated between 2001 and 2015 ( [[#Lund--2018a|Lund et al., 2018a]] ; [[#Paulot--2018b|Paulot et al., 2018b]] ). The sensitivity of NO <sub>3</sub> <sup>–</sup> to changes in NH <sub>3</sub> , SO <sub>4</sub> <sup>2–</sup> , and HNO <sub>3</sub> is determined primarily by aerosol pH, temperature, and aerosol liquid water ( [[#Guo--2016|Guo et al., 2016]] , 2018; [[#Weber--2016|Weber et al., 2016]] ; [[#Nenes--2020|Nenes et al., 2020]] ). In regions where aerosol pH is high, changes in NO <sub>3</sub> <sup>–</sup> follow changes in NO <sub>x</sub> emissions, consistent with the observed increase of ammonium nitrate in northern China from 2000–2015 ( [[#Wen--2018|Wen et al., 2018]] ) and its decrease in the US Central Valley ( [[#Pusede--2016|Pusede et al., 2016]] ). In contrast, the decrease in SO <sub>2</sub> emissions in the south-east USA has caused little change in NO <sub>3</sub> <sup>–</sup> <sub></sub> from 1998–2014 as nitric acid largely remains in the gas phase due to highly acidic aerosols ( [[#Weber--2016|Weber et al., 2016]] ; [[#Guo--2018|Guo et al., 2018]] ).

In summary, there is ''high confidence'' that the NH <sub>4</sub> <sup>+</sup> and NO <sub>3</sub> <sup>–</sup> burdens have increased from the pre-industrial period to the present day, although the magnitude of the increase is uncertain especially for NO <sub>3</sub> <sup>–</sup> . The sensitivity of NH <sub>4</sub> <sup>+</sup> and NO <sub>3</sub> <sup>–</sup> to changes in NH <sub>3</sub> , H <sub>2</sub> SO <sub>4</sub> and HNO <sub>3</sub> is well understood theoretically. However, it remains challenging to represent in models, in part because of uncertainties in the simulation of aerosol pH, and only a minority of ESMs consider nitrate aerosols in CMIP6.

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==== 6.3.5.3 Carbonaceous Aerosols ====

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Carbonaceous aerosols are black carbon (BC) <sup>[[#footnote-002|3]]</sup> , which is soot made almost purely of carbon, and organic aerosols <sup>[[#footnote-001|4]]</sup> (OA), which also contain hydrogen and oxygen and can be of both primary (POA) or secondary (SOA) origin. BC and a fraction of OA called brown carbon (BrC) absorb solar radiation. The various components of carbonaceous aerosols have different optical properties, so the knowledge of their partition, mixing, coating and ageing is essential to assess their climate effect ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.3.1.2|Section 7.3.3.1.2]] ).

Carbonaceous aerosols receive attention in the scientific and policy arena due to their radiative forcing, and their sizeable contribution to PM in an air-quality context (Rogelj et al. , 2014b; Harmsen et al. , 2015; Shindell et al. , 2016; Haines et al. , 2017; Myhre et al. , 2017) . BC exerts a positive forcing, but the forcing from carbonaceous aerosol as a whole is negative ( [[#Bond--2013|Bond et al., 2013]] ; [[#Thornhill--2021b|Thornhill et al., 2021b]] ). On average, carbonaceous aerosols account for 50–70% of PM with a diameter lower than 1 µm in polluted and pristine areas (Zhang et al. , 2007; Carslaw et al. , 2010; Andreae et al. , 2015; Monteiro dos Santos et al. , 2016; Chen et al. , 2017) .

An extensive review on BC ( [[#Bond--2013|Bond et al., 2013]] ) discussed limitations in inferring its atmospheric abundance and highlighted inconsistencies between different terminology and related measurement techniques ( [[#Petzold--2013|Petzold et al., 2013]] ; [[#Sharma--2017|Sharma et al., 2017]] ). Due to a lack of global observations, AR5 only reported declining total carbonaceous aerosol trends from the USA and a declining BC trend from the Arctic based on data available up to 2008. Since AR5, the number of observation sites has grown worldwide (Figure 6.7) but datasets suitable for global trend analyses remain limited ( [[#Reddington--2017|Reddington et al., 2017]] ; [[#Laj--2020|Laj et al., 2020]] ). Locally, studies based on observations from rural and background sites have reported decreasing surface carbonaceous aerosol trends in the Arctic, Europe, the USA, Japan and India (Table 6.6). Increases in carbonaceous aerosol concentrations in some rural sites of the western USA have been associated with wildfires ( [[#Hand--2013|Hand et al., 2013]] ; [[#Malm--2017|Malm et al., 2017]] ). Long-term OA observations are scarce, so their trends outside of the USA are difficult to assess. Ice-core analysis has provided insight into carbonaceous aerosol trends predating the satellite and observation era over the Northern Hemisphere ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.6|Section 2.2.6]] , Figure 2.9b).

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'''Table 6.6 |''' '''Summary of the regional carbonaceous aerosol trends at background observation sites.'''

{| class="wikitable"
|-
| Species

| Analysis Period

| Change/Trends

| References

|-
| rowspan="6"| BC

| 1990–2009

| Arctic Sites (Alert, Barrow, Ny-Alesund)

−2% yr <sup>−1</sup>

| [[#Sharma--2013|Sharma et al. (2013)]]

|-
| 1970–2010

| Finland (Kevo remote site)

−1.8% yr <sup>–1</sup>

| [[#Dutkiewicz--2014|Dutkiewicz et al. (2014)]]

|-
| 2005–2014

| Germany (rural site)

−2% yr <sup>–1</sup>

| [[#Kutzner--2018|Kutzner et al. (2018)]]

|-
| 2009–2016

| United Kingdom (Harwell rural site)

−8% yr <sup>–1</sup>

| [[#Singh--2018|Singh et al. (2018)]]

|-
| 2009–2019

| Japan (Fukue Island)

−5.8 ± 1.5% yr <sup>–1</sup>

| [[#Kanaya--2020|Kanaya et al. (2020)]]

|-
| 2009–2015

| India (Darjeeling mountain site)

−5% yr <sup>–1</sup>

| [[#Sarkar--2019|Sarkar et al. (2019)]]

|-
| OA

| 2001–2015

| USA (IMPROVE sites east of 100°W)

−2% yr <sup>–1</sup>

| [[#Malm--2017|Malm et al. (2017)]]

|-
| rowspan="2"| Total Carbon (EC + OC)

| 1990–2010

| USA (IMPROVE sites)

Western USA: −4 to −5% yr <sup>–1</sup>

Eastern USA: −1 to −2% yr <sup>–1</sup>

| [[#Hand--2013|Hand et al. (2013)]]

|-
| 2002–2010

| Spain (Montseny rural site)

−5% yr <sup>–1</sup>

| [[#Querol--2013|Querol et al. (2013)]]

|}

Knowledge of carbonaceous aerosol atmospheric abundance continues to rely on global models due to a lack of global-scale observations. For BC, models agree within a factor of two with measured surface mass concentrations in Europe and North America, but underestimate concentrations at the Arctic surface by one to two orders of magnitude, especially in winter and spring ( [[#Lee--2013|Lee et al., 2013]] ; [[#Lund--2018a|Lund et al., 2018a]] ). For OA, AeroCom models underestimate surface mass concentrations by a factor of two over urban areas, as their low horizontal resolution prevents them from resolving local pollution peaks ( [[#Tsigaridis--2014|Tsigaridis et al., 2014]] ; [[#Lund--2018a|Lund et al., 2018a]] ). Models agree within a factor of two with OA surface concentrations measured at remote sites, where surface concentrations are more spatially uniform ( [[#Tsigaridis--2014|Tsigaridis et al., 2014]] ).

BC and OA lifetimes are estimated to be 5.5 days ± 35% and 6.0 days ± 29% (median ± 1 standard deviation), respectively, based on an ensemble of 14 models ( [[#Gliß--2021|Gliß et al., 2021]] ). Disagreement in simulated lifetime leads to horizontal and vertical variations in predicted carbonaceous aerosol concentrations, with implications for radiative forcing ( [[#Samset--2013|Samset et al., 2013]] ; [[#Lund--2018b|Lund et al., 2018b]] ). Airborne campaigns have provided valuable vertical-profile measurements of carbonaceous aerosol concentrations (Schwarz et al. , 2013; Freney et al. , 2018; Hodgson et al. , 2018; Schulz et al. , 2019; D. Zhao et al. , 2019; Morgan et al. , 2020). Compared to those measurements, models tend to transport BC too high in the atmosphere, suggesting that lifetimes are not larger than 5.5 days ( [[#Samset--2013|Samset et al., 2013]] ; [[#Lund--2018b|Lund et al., 2018b]] ). Newly developed size-dependent wet-scavenging parametrization for BC ( [[#Taylor--2014|Taylor et al., 2014]] ; [[#Schroder--2015|Schroder et al., 2015]] ; [[#Ohata--2016|Ohata et al., 2016]] ; G. [[#Zhang--2017|]] [[#Zhang--2017|Zhang et al., 2017]] ; [[#Ding--2019|Ding et al., 2019]] ; [[#Moteki--2019|Moteki et al., 2019]] ; [[#Motos--2019|Motos et al., 2019]] ) may lead to decreased BC lifetimes and improve agreement with observed vertical profiles.

Simulated BC burdens show a large spread among models ( [[#Gliß--2021|Gliß et al., 2021]] ), despite using harmonised primary emissions, because of differences in BC removal efficiency linked to different treatment of ageing and mixing, particularly in strong source regions. The multi-model median BC burden for the year 2010 from [[#Gliß--2021|Gliß et al. (2021)]] , based on 14 AeroCom models, is 0.131 ± 0.047 Tg (median ± 1 standard deviation). The range encompasses values reported by independent single-model estimates ( Huang et al. , 2013; Lee et al. , 2013; Sharma et al. , 2013; Q. Wang et al. , 2014; Tilmes et al. , 2019) .

Simulated OA burdens also show a large spread among global models, with [[#Gliß--2021|Gliß et al. (2021)]] reporting a multi-model median of 1.91 ± 0.65 Tg for the year 2010. The large spread reflects the wide range in the complexity of the OA parametrizations, particularly for SOA formation, as well as in the primary OA emissions ( [[#Tsigaridis--2014|Tsigaridis et al., 2014]] ; [[#Gliß--2021|Gliß et al., 2021]] ). The uncertainties are particularly large in model estimates of SOA production rates, which vary between 10 and 143 Tg yr <sup>–1</sup> ( [[#Tsigaridis--2014|Tsigaridis et al., 2014]] ; [[#Hodzic--2016|Hodzic et al., 2016]] ; [[#Tilmes--2019|Tilmes et al., 2019]] ). While the level of complexity in the representation of OA in global models has increased since AR5 ( [[#Shrivastava--2017|Shrivastava et al., 2017]] ; [[#Hodzic--2020|Hodzic et al., 2020]] ), limitations in process-level understanding of the formation, ageing and removal of organic compounds lead to uncertainties in the global model predictions of global OA burden and distribution as well as the relative contribution of POA and SOA to OA. [[#Jo--2016|Jo et al. (2016)]] estimated that BrC contributes about 20% of total OA burden. That would give BrC a burden similar to that of BC ( ''low confidence'' ), enhancing the overall forcing exerted by carbonaceous aerosol absorption ( [[#Zhang--2020|Zhang et al., 2020]] ).

In summary, the lack of global-scale observations of carbonaceous aerosols, the complexity of processes influencing them, and the large spread in their simulated global budget and burdens means that there is only ''low confidence'' in the quantification of the present-day atmospheric distribution of individual components of carbonaceous aerosols. Global trends in carbonaceous aerosols cannot be characterized due to limited observations, but sites representative of background conditions have reported multi-year declines in BC over several regions of the Northern Hemisphere.

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