Editing IPCC:AR6/WGI/Chapter-7 (section)

=== 7.3.3 Aerosols ===

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Anthropogenic activity, and particularly burning of biomass and fossil fuels, has led to a substantial increase in emissions of aerosols and their precursors, and thus to increased atmospheric aerosol concentrations since the pre-industrial era (Sections 2.2.6 and 6.3.5, and Figure 2.9). This is particularly true for sulphate and carbonaceous aerosols (Section 6.3.5). This has in turn led to changes in the scattering and absorption of incoming solar radiation, and also affected cloud micro- and macro-physics and thus cloud radiative properties. Aerosol changes are heterogeneous in both space and time and have impacted not just Earth’s radiative energy budget but also air quality (Sections 6.1.1 and 6.6.2). Here, the assessment is focused exclusively on the global mean effects of aerosols on Earth’s energy budget, while regional changes and changes associated with individual aerosol compounds are assessed in ( [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Sections 6.4.1 and 6.4.2).

Consistent with the terminology introduced in Box 7.1, the ERF due to changes from direct aerosol–radiation interactions (ERFari) is equal to the sum of the instantaneous top-of-atmosphere (TOA) radiation change (IRFari) and the subsequent adjustments. Likewise, the ERF following interactions between anthropogenic aerosols and clouds (ERFaci, referred to as ‘indirect aerosol effects’ in previous assessment reports) can be divided into an instantaneous forcing component (IRFaci) due to changes in cloud droplet (and indirectly also ice crystal) number concentrations and sizes, and the subsequent adjustments of cloud water content or extent. While these changes are thought to be induced primarily by changes in the abundance of cloud condensation nuclei (CCN), a change in the number of ice nucleating particles (INPs) in the atmosphere may also have occurred, and thereby contributed to ERFaci by affecting properties of mixed-phase and cirrus (ice) clouds. In the following, an assessment of IRFari and ERFari ( [[#7.3.3.1|Section 7.3.3.1]] ) focusing on observation-based ( [[#7.3.3.1.1|Section 7.3.3.1.1]] ) as well as model-based ( [[#7.3.3.1.2|Section 7.3.3.1.2]] ) evidence is presented. The same lines of evidence are presented for IRFaci and ERFaci in [[#7.3.3.2|Section 7.3.3.2]] . These lines of evidence are then compared with TOA energy budget constraints on the total aerosol ERf ( [[#7.3.3.3|Section 7.3.3.3]] ) before an overall assessment of the total aerosol ERF is given in [[#7.3.3.4|Section 7.3.3.4]] . For the model-based evidence, all estimates are generally valid for 2014 relative to 1750 (the time period spanned by CMIP6 historical simulations), while for observation-based evidence the assessed studies use slightly different end points, but they all generally fall within a decade (2010–2020).

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==== 7.3.3.1 Aerosol–Radiation Interactions ====

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Since AR5, deeper understanding of the processes that govern aerosol radiative properties, and thus IRFari, has emerged. Combined with new insights into adjustments to aerosol forcing, this progress has informed new observation- and model-based estimates of ERFari and associated uncertainties.

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===== 7.3.3.1.1 Observation-based lines of evidence =====

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Estimating IRFari requires an estimate of industrial-era changes in aerosol optical depth (AOD) and absorption AOD, which are often taken from global aerosol model simulations. Since AR5, updates to methods of estimating IRFari based on aerosol remote sensing or data-assimilated reanalyses of atmospheric composition have been published. [[#Ma--2014|Ma et al. (2014)]] applied the method of [[#Quaas--2008|Quaas et al. (2008)]] to updated broadband radiative flux measurements from CERES, MODIS-retrieved AODs, and modelled anthropogenic aerosol fractions to find a clear-sky IRFari of −0.6 W m <sup>−2</sup> . This would translate into an all-sky estimate of about −0.3 W m <sup>−2</sup> based on the clear-sky to all-sky ratio implied by [[#Kinne--2019|Kinne (2019)]] . [[#Rémy--2018|Rémy et al. (2018)]] applied the methods of [[#Bellouin--2013a|Bellouin et al. (2013a)]] to the reanalysis by the Copernicus Atmosphere Monitoring Service, which assimilates MODIS total AOD. Their estimate of IRFari varies between −0.5 W m <sup>–2</sup> and −0.6 W m <sup>−2</sup> over the period 2003–2018, and they attribute those relatively small variations to variability in biomass-burning activity. [[#Kinne--2019|Kinne (2019)]] provided updated monthly total AOD and absorption AOD climatologies, obtained by blending multi-model averages with ground-based sun-photometer retrievals, to find a best estimate of IRFari of −0.4 W m <sup>−2</sup> . The updated IRFari estimates above are all scattered around the midpoint of the IRFari range of −0.35 ± 0.5 W m <sup>−2</sup> assessed by AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ).

The more negative estimate of [[#Rémy--2018|Rémy et al. (2018)]] is due to neglecting a small positive contribution from absorbing aerosols above clouds and obtaining a larger anthropogenic fraction than [[#Kinne--2019|Kinne (2019)]] . [[#Rémy--2018|Rémy et al. (2018)]] also did not update their assumptions on black carbon anthropogenic fraction and its contribution to absorption to reflect recent downward revisions ( [[#7.3.3.1.2|Section 7.3.3.1.2]] ). [[#Kinne--2019|Kinne (2019)]] made those revisions, so more weight is given to that study to assess the central estimate of satellite-based IRFari to be only slightly stronger than reported in AR5 at –0.4 W m <sup>–2</sup> . While uncertainties in the anthropogenic fraction of total AOD remain, improved knowledge of anthropogenic absorption results in a slightly narrower ''very likely'' range here than in AR5. The assessed best estimate and ''very'' ''likely'' IRFari range from observation-based evidence is therefore –0.4 ± 0.4 W m <sup>–2</sup> , but with ''medium confidence'' due to the limited number of studies available ''.''

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===== 7.3.3.1.2 Model-based lines of evidence =====

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While observation-based evidence can be used to estimate IRFari, global climate models are needed to calculate the associated adjustments and the resulting ERFari, using the methods described in [[#7.3.1|Section 7.3.1]] .

A range of developments since AR5 affect model-based estimates of IRFari. Global emissions of most major aerosol compounds and their precursors are found to be higher in the current inventories, and with increasing trends. Emissions of the sulphate precursor SO <sub>2</sub> are a notable exception; they are similar to those used in AR5 and approximately time-constant in recent decades ( [[#Hoesly--2018|Hoesly et al., 2018]] ). [[#Myhre--2017|Myhre et al. (2017)]] showed, in a multi-model experiment, that the net result of these revised emissions is an IRFari trend that is relatively flat in recent years (post-2000), a finding confirmed by a single-model study by [[#Paulot--2018|Paulot et al. (2018)]] .

In AR5, the assessment of the black carbon (BC) contribution to IRFari was markedly strengthened in confidence by the review by [[#Bond--2013|Bond et al. (2013)]] , where a key finding was a perceived model underestimate of atmospheric absorption when compared to Aeronet observations ( [[#Boucher--2013|Boucher et al., 2013]] ). This assessment has since been revised considering: new knowledge on the effect of the temporal resolution of emissions inventories ( [[#Wang--2016|Wang et al., 2016]] ); the representativeness of Aeronet sites ( [[#Wang--2018|Wang et al., 2018]] ); issues with comparing absorption retrieval to models (E. [[#Andrews--2017|]] [[#Andrews--2017|Andrews et al., 2017]] ); and the ageing ( [[#Peng--2016|Peng et al., 2016]] ), lifetime ( [[#Lund--2018b|Lund et al., 2018b]] ) and average optical parameters ( [[#Zanatta--2016|Zanatta et al., 2016]] ) of BC. Consistent with these updates, [[#Lund--2018a|Lund et al. (2018a)]] estimated the net IRFari in 2014 (relative to 1750) to be –0.17 W m <sup>–2</sup> , using CEDS emissions ( [[#Hoesly--2018|Hoesly et al., 2018]] ) as input to a chemical transport model. They attributed the weaker estimate relative to AR5 (–0.35 ± 0.5 W m <sup>–2</sup> ; [[#Myhre--2013a|Myhre et al., 2013a]] ) to stronger absorption by organic aerosol, updated parametrization of BC absorption, and slightly reduced sulphate cooling. Broadly consistent with [[#Lund--2018a|Lund et al. (2018a)]] , another single-model study by [[#Petersik--2018|Petersik et al. (2018)]] estimated an IRFari of –0.19 W m <sup>–2</sup> . Another single-model study by [[#Lurton--2020|Lurton et al. (2020)]] reported a more negative estimate at –0.38 W m <sup>–2</sup> , but is given less weight here because the model lacked interactive aerosols and instead used prescribed climatological aerosol concentrations.

The above estimates support a less negative central estimate and a slightly narrower range compared to those reported for IRFari from ESMs in AR5 of –0.35 [–0.6 to –0.13] W m <sup>–2</sup> . The assessed central estimate and ''very likely'' IRFari range from model-based evidence alone is therefore –0.2 ± 0.2 W m <sup>–2</sup> for 2014 relative to 1750, with ''medium confidence'' due to the limited number of studies available. Revisions due to stronger organic aerosol absorption, further developed BC parameterizations and somewhat reduced sulphate emissions in recent years.

Since AR5 considerable progress has been made in the understanding of adjustments in response to a wide range of climate forcings, as discussed in ( [[#7.3.1|Section 7.3.1]] . The adjustments in ERFari are principally caused by cloud changes, but also by lapse rate and atmospheric water vapour changes, all mainly associated with absorbing aerosols like BC. [[#Stjern--2017|Stjern et al. (2017)]] found that for BC, about 30% of the (positive) IRFari is offset by adjustments of clouds (specifically, an increase in low-clouds and decrease in high-clouds) and lapse rate, by analysing simulations by five Precipitation Driver Response Model Intercomparison Project (PDRMIP) models. [[#Smith--2018b|Smith et al. (2018b)]] considered more models participating in PDRMIP and suggested that about half the IRFari was offset by adjustments for BC, a finding generally supported by single-model studies ( [[#Takemura--2019|Takemura and Suzuki, 2019]] ; [[#Zhao--2019|Zhao and Suzuki, 2019]] ). [[#Thornhill--2021b|Thornhill et al. (2021b)]] also reported a negative adjustment for BC based on AerChemMIP ( [[#Collins--2017|Collins et al., 2017]] ) but found it to be somewhat smaller in magnitude than those reported in [[#Smith--2018b|Smith et al. (2018b)]] and [[#Stjern--2017|Stjern et al. (2017)]] . In contrast, [[#Allen--2019|Allen et al. (2019)]] found a positive adjustment for BC and suggested that most models simulate negative adjustment for BC because of a misrepresentation of aerosol atmospheric heating profiles.

[[#Zelinka--2014|Zelinka et al. (2014)]] used the approximate partial radiation perturbation technique to quantify the ERFari in 2000 relative to 1860 in nine CMIP5 models; they estimated the ERFari (accounting for a small contribution from longwave radiation) to be –0.27 ± 0.35 W m <sup>–2</sup> . However, it should be noted that in [[#Zelinka--2014|Zelinka et al. (2014)]] adjustments of clouds caused by absorbing aerosols through changes in the thermal structure of the atmosphere (termed the semidirect effect of aerosols in AR5) are not included in ERFari but in ERFaci. The corresponding estimate emerging from the Radiative Forcing Model Intercomparison Project (RFMIP, [[#Pincus--2016|Pincus et al., 2016]] ) is –0.25 ± 0.40 W m <sup>–2</sup> ( [[#Smith--2020b|Smith et al., 2020b]] ), which is generally supported by single-model studies published since AR5 ( [[#Zhang--2016|Zhang et al., 2016]] ; [[#Fiedler--2017|Fiedler et al., 2017]] ; [[#Nazarenko--2017|Nazarenko et al., 2017]] ; [[#Zhou--2017c|Zhou et al., 2017c]] , 2018b; [[#Grandey--2018|Grandey et al., 2018]] ). A 5% inflation is applied to the CMIP5 and CMIP6 fixed-SST derived estimates of ERFari from [[#Zelinka--2014|Zelinka et al. (2014)]] and [[#Smith--2020b|Smith et al. (2020b)]] to account for land surface cooling (Table 7.6). Based on the above, ERFari from model-based evidence is assessed to be –0.25 ± 0.25 W m <sup>–2</sup> .

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===== 7.3.3.1.3 Overall assessment of IRFari and ERFari =====

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The observation-based assessment of IRFari of –0.4 ± 0.4 W m <sup>–2</sup> and the corresponding model-based assessment of –0.2 ± 0.2 W m <sup>–2</sup> can be compared to the range of –0.45 to –0.05 W m <sup>–2</sup> that emerged from a comprehensive review in which an observation-based estimate of anthropogenic AOD was combined with model-derived ranges for all relevant aerosol radiative properties ( [[#Bellouin--2020|Bellouin et al., 2020]] ). Based on the above, IRFari is assessed to be –0.25 ± 0.2 W m <sup>–2</sup> ( ''medium confidence'' ).

ERFari from model-based evidence is –0.25 ± 0.25 W m <sup>–2</sup> , which suggests a small negative adjustment relative to the model-based IRFari estimate, consistent with the literature discussed in ( [[#7.3.3.1.2|Section 7.3.3.1.2]] . Adding this small adjustment to our assessed IRFari estimate of –0.25 W m <sup>–2</sup> , and accounting for additional uncertainty in the adjustments, ERFari is assessed to –0.3 ± 0.3 ( ''medium confidence'' ). This assessment is consistent with the 5–95% confidence range for ERFari in [[#Bellouin--2020|Bellouin et al. (2020)]] of –0.71 to –0.14 W m <sup>–2</sup> , and notably implies that it is ''very likely'' that ERFari is negative. Differences relative to [[#Bellouin--2020|Bellouin et al. (2020)]] reflect the range of estimates in Table 7.6 and the fact that an ERFari more negative than –0.6 W m <sup>–2</sup> would require adjustments that considerably augment the assessed IRFari, which is not supported by the assessed literature.

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'''Table 7.6''' '''|''' '''Present-day effective radiative forcing (ERF) due to changes in aerosol–radiation interactions (ERFari) and changes in aerosol–cloud interactions (ERFaci), and total aerosol ERF (ERFari+aci)''' from GCM CMIP6 (2014 relative to 1850; [[#Smith--2020b|Smith et al., 2020b]] and later model results) and CMIP5 (year 2000 relative to 1860; [[#Zelinka--2014|Zelinka et al., 2014]] ). CMIP6 results are simulated as part of RFMIP ( [[#Pincus--2016|Pincus et al., 2016]] ). An additional 5% is applied to the CMIP5 and CMIP6 model results to account for land-surface cooling (Figure 7.4; [[#Smith--2020a|Smith et al., 2020a]] ).

{| class="wikitable"
|-
| Models

| ERFari

(W m <sup>–2</sup> )

| ERFaci

(W m <sup>–2</sup> )

| ERFari+aci

(W m <sup>–2</sup> )

|-
| ACCESS-CM2

| –0.24

| –0.93

| –1.17

|-
| ACCESS-ESM1-5

| –0.07

| –1.19

| –1.25

|-
| BCC-ESM1

| –0.79

| –0.69

| –1.48

|-
| CanESM5

| –0.02

| –1.09

| –1.11

|-
| CESM2

| +0.15

| –1.65

| –1.50

|-
| CNRM-CM6-1

| –0.28

| –0.86

| –1.14

|-
| CNRM-ESM2-1

| –0.15

| –0.64

| –0.79

|-
| EC-Earth3

| –0.39

| –0.50

| –0.89

|-
| GFDL-CM4

| –0.12

| –0.72

| –0.84

|-
| GFDL-ESM4

| –0.06

| –0.84

| –0.90

|-
| GISS-E2-1-G (physics_version=1)

| –0.55

| –0.81

| –1.36

|-
| GISS-E2-1-G (physics_version=3)

| –0.64

| –0.39

| –1.02

|-
| HadGEM3-GC31-LL

| –0.29

| –0.87

| –1.17

|-
| IPSL-CM6A-LR

| –0.39

| –0.29

| –0.68

|-
| IPSL-CM6A-LR-INCA

| –0.45

| –0.35

| –0.80

|-
| MIROC6

| –0.22

| –0.77

| –0.99

|-
| MPI-ESM-1-2-HAM

| +0.10

| –1.40

| –1.31

|-
| MRI-ESM2-0

| –0.48

| –0.74

| –1.22

|-
| NorESM2-LM

| –0.15

| –1.08

| –1.23

|-
| NorESM2-MM

| –0.03

| –1.26

| –1.29

|-
| UKESM1-0-LL

| –0.20

| –0.99

| –1.19

|-
| CMIP6 average and 5–95% confidence range (2014 relative to 1850)

| –0.25 ± 0.40

| –0.86 ± 0.57

| –1.11 ± 0.38

|-
| CMIP5 average and 5–95% confidence range (2000 relative to 1860)

| –0.27 ± 0.35

| –0.96 ± 0.55

| –1.23 ± 0.48

|}

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==== 7.3.3.2 Aerosol–Cloud Interactions ====

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Anthropogenic aerosol particles primarily affect water clouds by serving as additional cloud condensation nuclei (CCN) and thus increasing cloud drop number concentration (N <sub>d</sub> ; [[#Twomey--1959|Twomey, 1959]] ). Increasing N <sub>d</sub> while holding liquid water content constant reduces cloud drop effective radius (r <sub>e</sub> ), increases the cloud albedo, and induces an instantaneous negative radiative forcing (IRFaci). The clouds are thought to subsequently adjust by a slowing of the drop coalescence rate, thereby delaying or suppressing rainfall. Rain generally reduces cloud lifetime and thereby liquid water path (LWP, i.e., the vertically integrated cloud water) and/or cloud fractional coverage (Cf; [[#Albrecht--1989|Albrecht, 1989]] ), thus any aerosol-induced rain delay or suppression would be expected to increase LWP and/or Cf. Such adjustments could potentially lead to an ERFaci considerably larger in magnitude than the IRFaci alone. However, adding aerosols to non-precipitating clouds has been observed to have the opposite effect (i.e., a reduction in LWP and/or Cf) ( [[#Lebsock--2008|Lebsock et al., 2008]] ; [[#Christensen--2011|Christensen and Stephens, 2011]] ). These findings have been explained by enhanced evaporation of the smaller droplets in the aerosol-enriched environments, and resultant enhanced mixing with ambient air, leading to cloud dispersal.

A small subset of aerosols can also serve as ice nucleating particles (INPs) that initiate the ice phase in supercooled water clouds, and thereby alter cloud radiative properties and/or lifetimes. However, the ability of anthropogenic aerosols (specifically BC) to serve as INPs in mixed-phase clouds has been found to be negligible in recent laboratory studies (e.g., [[#Vergara-Temprado--2018|Vergara-Temprado et al., 2018]] ). No assessment of the contribution to ERFaci from cloud phase changes induced by anthropogenic INPs will therefore be presented.

In ice (cirrus) clouds (cloud temperatures less than –40°C), INPs can initiate ice crystal formation at relative humidity much lower than that required for droplets to freeze spontaneously. Anthropogenic INPs can thereby influence ice crystal numbers and thus cirrus cloud radiative properties. At cirrus temperatures, certain types of BC have in fact been demonstrated to act as INPs in laboratory studies ( [[#Ullrich--2017|Ullrich et al., 2017]] ; [[#Mahrt--2018|Mahrt et al., 2018]] ), suggesting a non-negligible anthropogenic contribution to INPs in cirrus clouds. Furthermore, anthropogenic changes to drop number also alter the number of droplets available for spontaneous freezing, thus representing a second pathway through which anthropogenic emissions could affect cirrus clouds.

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===== 7.3.3.2.1 Observation-based evidence =====

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Since AR5, the analysis of observations to investigate aerosol–cloud interactions has progressed along several axes: (i) The framework of forcing and adjustments introduced rigorously in AR5 has helped better categorize studies; (ii) the literature assessing statistical relationships between aerosol and cloud in satellite retrievals has grown, and retrieval uncertainties are better characterized; (iii) advances have been made to infer causality in aerosol–cloud relationships.

In AR5 the statistical relationship between cloud microphysical properties and aerosol index (AI; AOD multiplied by Ångström exponent) was used to make inferences about IRFaci were assessed alongside other studies which related cloud quantities to AOD. However, it is now well-documented that the latter approach leads to low estimates of IRFaci since AOD is a poor proxy for cloud-base CCN ( [[#Penner--2011|Penner et al., 2011]] ; [[#Stier--2016|Stier, 2016]] ). [[#Gryspeerdt--2017|Gryspeerdt et al. (2017)]] demonstrated that the statistical relationship between droplet concentration and AOD leads to an inferred IRFaci that is underestimated by at least 30%, while the use of AI leads to estimates of IRFaci to within ±20%, if the anthropogenic perturbation of AI is known.

Further, studies assessed in AR5 mostly investigated linear relationships between cloud droplet concentration and aerosol ( [[#Boucher--2013|Boucher et al., 2013]] ). Since in most cases the relationships are not linear, this leads to a bias ( [[#Gryspeerdt--2016|Gryspeerdt et al., 2016]] ). Several studies did not relate cloud droplet concentration, but cloud droplet effective radius, to the aerosol ( [[#Brenguier--2000|Brenguier et al., 2000]] ). This is problematic because in order to infer IRFaci, stratification by cloud LWP is required ( [[#McComiskey--2012|McComiskey and Feingold, 2012]] ). Where LWP positively co-varies with aerosol retrievals (which is often the case), IRFaci inferred from such relationships is biased towards low values. Also, it is increasingly evident that different cloud regimes show different sensitivities to aerosols ( [[#Stevens--2009|Stevens and Feingold, 2009]] ). Averaging statistics over regimes thus biases the inferred IRFaci ( [[#Gryspeerdt--2014b|Gryspeerdt et al., 2014b]] ). The AR5 concluded that IRFaci estimates tied to satellite studies generally show weak IRFaci ( [[#Boucher--2013|Boucher et al., 2013]] ), but when correcting for the biases discussed above, this is no longer the case.

Since AR5, several studies assessed the global IRFaci from satellite observations using different methods (Table 7.7). All studies relied on statistical relationships between aerosol and cloud quantities to infer sensitivities. Four studies inferred IRFaci by estimating the anthropogenic perturbation of N <sub>d</sub> (cloud drop number concentration). For this, [[#Bellouin--2013b|Bellouin et al. (2013b)]] and [[#Rémy--2018|Rémy et al. (2018)]] made use of regional-seasonal regressions between satellite-derived N <sub>d</sub> and AOD following [[#Quaas--2008|Quaas et al. (2008)]] , while [[#Gryspeerdt--2017|Gryspeerdt et al. (2017)]] used AI instead of AOD in the regression to infer IRFaci. [[#McCoy--2017b|McCoy et al. (2017b)]] instead used the sulphate-specific mass derived in the MERRA aerosol reanalysis that assimilated MODIS AOD ( [[#Rienecker--2011|Rienecker et al., 2011]] ). All approaches have in common the need to identify the anthropogenic perturbation of the aerosol to assess IRFaci. [[#Gryspeerdt--2017|Gryspeerdt et al. (2017)]] and [[#Rémy--2018|Rémy et al. (2018)]] used the same approach as [[#Bellouin--2013b|Bellouin et al. (2013b)]] , while [[#McCoy--2017b|McCoy et al. (2017b)]] used an anthropogenic fraction from the AEROCOM multi-model ensemble ( [[#Schulz--2006|Schulz et al., 2006]] ). [[#Chen--2014|Chen et al. (2014)]] , [[#Christensen--2016a|Christensen et al. (2016a)]] and [[#Christensen--2017|Christensen et al. (2017)]] derived the combination of IRFaci and the LWP adjustment to IRFaci (‘intrinsic forcing’ in their terminology). They relate AI and cloud albedo statistically and use the anthropogenic aerosol fraction from [[#Bellouin--2013b|Bellouin et al. (2013b)]] . This was further refined by [[#Hasekamp--2019|Hasekamp et al. (2019)]] who used additional polarimetric satellite information over ocean to obtain a better proxy for CCN. They derived an IRFaci of –1.14 [–1.72 to –0.84] W m <sup>–2</sup> . The variant by [[#Christensen--2017|Christensen et al. (2017)]] is an update compared to the [[#Chen--2014|Chen et al. (2014)]] and [[#Christensen--2016a|Christensen et al. (2016a)]] studies in that it better accounts for ancillary influences on the aerosol retrievals such as aerosol swelling and three-dimensional radiative effects. [[#McCoy--2020|McCoy et al. (2020)]] used the satellite-observed hemispheric difference in N <sub>d</sub> as an emergent constraint on IRFaci as simulated by GCMs to obtain a range of –1.2 to –0.6 W m <sup>–2</sup> (95% confidence interval). [[#Diamond--2020|Diamond et al. (2020)]] analysed the difference in clouds affected by ship emissions with unperturbed clouds and based on this inferred a global IRFaci of –0.69 [–0.99 to –0.44] W m <sup>–2</sup> .

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'''Table 7.''' '''7 |''' '''Studies quantifying aspects of the global effective radiative forcing due to aerosol–cloud interactions ERFaci that are mainly based on satellite retrievals and were published since AR5.''' All forcings/adjustments are presented as global annual mean values in W m <sup>–2</sup> . Most studies split the ERFaci into instantaneous radiative forcing (IRFaci) and adjustments in liquid water path (LWP) and cloud fraction (Cf) separately. All published studies only considered liquid clouds. Some studies assessed the IRFaci and the LWP adjustment together and called this ‘intrinsic forcing’ ( [[#Christensen--2017|Christensen et al., 2017]] ) and the cloud fraction adjustment ‘extrinsic forcing’. Published uncertainty ranges are converted to 5–95% confidence intervals, and ‘n/a’ indicates that the study did not provide an estimate for the relevant IRF/ERF.

{| class="wikitable"
|-
| IRFaci (W m <sup>–2</sup> )

| Liquid Water Path (LWP) Adjustment (W m <sup>–2</sup> )

| Cloud Fraction (Cf) Adjustment (W m <sup>–2</sup> )

| Reference

|-
| –0.6 ± 0.6

| n/a

| n/a

| [[#Bellouin--2013b|Bellouin et al. (2013b)]]

|-
| –0.4 [–0.2 to –1.0]

| n/a

| n/a

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

|-
| –1.0 ± 0.4

| n/a

| n/a

| [[#McCoy--2017b|McCoy et al. (2017b)]]

|-
| n/a

| n/a

| –0.5 [–0.1 to –0.6]

| [[#Gryspeerdt--2016|Gryspeerdt et al. (2016)]]

|-
| n/a

| +0.3 to 0.0

| n/a

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

|-
| –0.8 ± 0.7

| n/a

| n/a

| [[#Rémy--2018|Rémy et al. (2018)]]

|-
| –0.53

–1.14 [–1.72 to –0.84]

–1.2 to –0.6

–0.69 [–0.99 to –0.44]

| +0.15

n/a

n/a

n/a

| n/a

n/a

n/a

n/a

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

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

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

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

|-
| colspan="2"| ‘Intrinsic Forcing’

|
|-
| colspan="2"| –0.5 ± 0.5

| –0.5 ± 0.5

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

|-
| colspan="2"| –0.4 ± 0.3

| n/a

| [[#Christensen--2016a|Christensen et al. (2016a)]]

|-
| colspan="2"| –0.3 ± 0.4

| –0.4 ± 0.5

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

|}

Summarizing the above findings related to statistical relationships and causal aerosol effects on cloud properties, there is ''high confidence'' that anthropogenic aerosols lead to an increase in cloud droplet concentrations. Taking the average across the studies providing IRFaci estimates discussed above and considering the general agreement among estimates (Table 7.7), IRFaci is assessed to be –0.7 ± 0.5 W m <sup>–2</sup> ( ''medium confidence'' ).

Multiple studies have found a positive relationship between cloud fraction and/or cloud LWP and aerosols (e.g., Nakajimaet al., 2001; [[#Kaufman--2006|Kaufman and Koren, 2006]] ; [[#Quaas--2009|Quaas et al., 2009]] ). Since AR5, however, it has been documented that factors independent of causal aerosol–cloud interactions heavily influence such statistical relationships. These include the swelling of aerosols in the high relative humidity in the vicinity of clouds ( [[#Grandey--2013|Grandey et al., 2013]] ) and the contamination of aerosol retrievals next to clouds by cloud remnants and cloud-side scattering ( [[#Várnai--2015|Várnai and Marshak, 2015]] ; [[#Christensen--2017|Christensen et al., 2017]] ). Stratifying relationships by possible influencing factors such as relative humidity ( [[#Koren--2010|Koren et al., 2010]] ) does not yield satisfying results since observations of the relevant quantities are not available at the resolution and quality required. Another approach to tackle this problem was to assess the relationship of cloud fraction with droplet concentration ( [[#Gryspeerdt--2016|Gryspeerdt et al., 2016]] ; [[#Michibata--2016|Michibata et al., 2016]] ; [[#Sato--2018|Sato et al., 2018]] ). The relationship between satellite-retrieved cloud fraction and N <sub>d</sub> was found to be positive ( [[#Christensen--2016a|Christensen et al., 2016a]] , 2017; [[#Gryspeerdt--2016|Gryspeerdt et al., 2016]] ), implying an overall adjustment that leads to a more negative ERFaci. However, since retrieved N <sub>d</sub> is biased low for broken clouds this result has been called into question ( [[#Grosvenor--2018|Grosvenor et al., 2018]] ). [[#Zhu--2018|Zhu et al. (2018)]] proposed to circumvent this problem by considering N <sub>d</sub> of only continuous thick cloud covers, on the basis of which [[#Rosenfeld--2019|Rosenfeld et al. (2019)]] still obtained a positive relationship between cloud fraction and N <sub>d</sub> relationship.

The relationship between LWP and cloud droplet number is debated. Most recent studies (primarily based on MODIS data) find negative statistical relationships ( [[#Michibata--2016|Michibata et al., 2016]] ; [[#Toll--2017|Toll et al., 2017]] ; [[#Sato--2018|Sato et al., 2018]] ; [[#Gryspeerdt--2019|Gryspeerdt et al., 2019]] ), while [[#Rosenfeld--2019|Rosenfeld et al. (2019)]] obtained a modest positive relationship. To increase confidence that observed relationships between aerosol emissions and cloud adjustments are causal, known emissions of aerosols and aerosol precursor gases into otherwise pristine conditions have been exploited. Ship exhaust is one such source. [[#Goren--2014|Goren and Rosenfeld (2014)]] suggested that both LWP and Cf increase in response to ship emissions, contributing approximately 75% to the total ERFaci in mid-latitude stratocumulus. [[#Christensen--2011|Christensen and Stephens (2011)]] found that such strong adjustments occur for open-cell stratocumulus regimes, while adjustments are comparatively small in closed-cell regimes. Volcanic emissions have been identified as another important source of information ( [[#Gassó--2008|Gassó, 2008]] ). From satellite observations, [[#Yuan--2011|Yuan et al. (2011)]] documented substantially larger Cf, higher cloud tops, reduced precipitation likelihood, and increased albedo in cumulus clouds in the plume of the Kīlauea volcano in Hawaii. [[#Ebmeier--2014|Ebmeier et al. (2014)]] confirmed the increased LWP and albedo for other volcanoes. In contrast, for the large Holuhraun eruption in Iceland, [[#Malavelle--2017|Malavelle et al. (2017)]] did not find any large-scale change in LWP in satellite observations. However, when accounting for meteorological conditions, [[#McCoy--2018|McCoy et al. (2018)]] concluded that for cyclonic conditions, the extra Holuhraun aerosol did enhance LWP. [[#Toll--2017|Toll et al. (2017)]] examined a large sample of volcanoes and found a distinct albedo effect, but only modest LWP changes, on average. [[#Gryspeerdt--2019|Gryspeerdt et al. (2019)]] demonstrated that the negative LWP–N <sub>d</sub> relationship becomes very small when conditioned on a volcanic eruption, and therefore concluded that LWP adjustments are small in most regions. Similarly, [[#Toll--2019|Toll et al. (2019)]] studied clouds downwind of various anthropogenic aerosol sources using satellite observations and inferred an IRFaci of –0.52 W m <sup>–2</sup> that was partly offset by 29% due to aerosol-induced LWP decreases.

Apart from adjustments involving LWP and Cf, several studies have also documented a negative relationship between cloud-top temperature and AOD/AI in satellite observations (e.g., [[#Koren--2005|Koren et al., 2005]] ). [[#Wilcox--2016|Wilcox et al. (2016)]] proposed that this could be explained by black-carbon (BC) absorption reducing boundary-layer turbulence, which in turn could lead to taller clouds. However, it has been demonstrated that the satellite-derived relationships are affected by spurious co-variation ( [[#Gryspeerdt--2014a|Gryspeerdt et al., 2014a]] ), and it therefore remains unclear whether a systematic causal effect exists.

Identifying relationships between INP concentrations and cloud properties from satellites is intractable because the INPs generally represent a very small subset of the overall aerosol population at any given time or location. For ice clouds, only a few satellite studies have so far investigated responses to aerosol perturbations. [[#Gryspeerdt--2018|Gryspeerdt et al. (2018)]] find a positive relationship between aerosol and ice crystal number for cold cirrus under strong dynamical forcing, which could be explained by an overall larger number of solution droplets available for homogeneous freezing in polluted regions. [[#Zhao--2018|Zhao et al. (2018)]] conclude that the sign of the relationship between ice crystal size and aerosol depends on humidity. While these studies support modelling results finding that ice clouds do respond to anthropogenic aerosols ( [[#7.3.3.2.2|Section 7.3.3.2.2]] ), no quantitative conclusions about IRFaci or ERFaci for ice clouds can be drawn based on satellite observations.

Only a handful of studies have estimated the LWP and Cf adjustments that are needed for satellite-based estimates of ERFaci. [[#Chen--2014|Chen et al. (2014)]] and [[#Christensen--2017|Christensen et al. (2017)]] used the relationship between cloud fraction and AI to infer the cloud fraction adjustment. [[#Gryspeerdt--2017|Gryspeerdt et al. (2017)]] used a similar approach but tried to account for non-causal coorelations between aerosols and cloud fraction by using N <sub>d</sub> <sup></sup> as a mediating factor. These three studies together suggest a global Cf adjustment that augments ERFaci relative to IRFaci by –0.5 ± 0.4 W m <sup>–2</sup> ( ''medium confidence'' ). For global estimates of the LWP adjustment, evidence is even scarcer. [[#Gryspeerdt--2019|Gryspeerdt et al. (2019)]] derived an estimate of the LWP adjustment using a method similar to [[#Gryspeerdt--2016|Gryspeerdt et al. (2016)]] . They estimated that the LWP adjustment offsets 0–60% of the (negative) IRFaci (0.0 to +0.3 W m <sup>–2</sup> ). Supporting an offsetting LWP adjustment, [[#Toll--2019|Toll et al. (2019)]] estimated a moderate LWP adjustment of 29% (+0.15 W m <sup>–2</sup> ). The adjustment due to LWP is assessed to be small, with a central estimate and ''very likely'' range of 0.2 ± 0.2 W m <sup>–2</sup> , but with ''low confidence'' due to the limited number of studies available.

Combining IRFaci and the associated adjustments in Cf and LWP (adding uncertainties in quadrature), considering only liquid-water clouds and evidence from satellite observations alone, the central estimate and ''very likely'' range for ERFaci is assessed to be –1.0 ± 0.7 W m <sup>–2</sup> ( ''medium confidence'' ). The confidence level and wider range for ERFaci compared to IRFaci reflect the relatively large uncertainties that remain in the adjustment contribution to ERFaci.

<div id="7.3.3.2.2" class="h4-container"></div>

<span id="model-based-evidence"></span>
===== 7.3.3.2.2 Model-based evidence =====

<div id="h4-5-siblings" class="h4-siblings"></div>

As in AR5, the representation of aerosol–cloud interactions in ESMs remains a challenge, due to the limited representation of important sub-gridscale processes, from the emissions of aerosols and their precursors to precipitation formation. ESMs that simulate ERFaci typically include aerosol–cloud interactions in liquid stratiform clouds only, while very few include aerosol interactions with mixed-phase, convective and ice clouds. Adding to the spread in model-derived estimates of ERFaci is the fact that model configurations and assumptions vary across studies, for example when it comes to the treatment of oxidants, which influence aerosol formation, and their changes through time ( [[#Karset--2018|Karset et al., 2018]] ).

In AR5, ERFaci was assessed as the residual of the total aerosol ERF and ERFari, as the total aerosol ERF was easier to calculate based on available model simulations ( [[#Boucher--2013|Boucher et al., 2013]] ). The central estimates of total aerosol ERF and ERFari in AR5 were –0.9 W m <sup>–2</sup> and –0.45 W m <sup>–2</sup> , respectively, yielding an ERFaci estimate of –0.45 W m <sup>–2</sup> . This value is much less negative than the bottom-up estimate of ERFaci from ESMs presented in AR5 (–1.4 W m <sup>–2</sup> ) and efforts have been made since to reconcile this difference. [[#Zelinka--2014|Zelinka et al. (2014)]] estimated ERFaci to be –0.96 ± 0.55 W m <sup>–2</sup> (including semi-direct effects, and with land-surface cooling effect applied), based on nine CMIP5 models (Table 7.6). The corresponding ERFaci estimate based on 17 RFMIP models from CMIP6 is slightly less negative at –0.86 ± 0.57 W m <sup>–2</sup> (Table 7.6). Other post-AR5 estimates of ERFaci based on single-model studies are either in agreement with or slightly larger in magnitude than the CMIP6 estimate ( [[#Gordon--2016|Gordon et al., 2016]] ; [[#Fiedler--2017|Fiedler et al., 2017]] , 2019; [[#Neubauer--2017|Neubauer et al., 2017]] ; [[#Karset--2018|Karset et al., 2018]] ; [[#Regayre--2018|Regayre et al., 2018]] ; [[#Zhou--2018b|Zhou et al., 2018b]] ; [[#Golaz--2019|Golaz et al., 2019]] ; [[#Diamond--2020|Diamond et al., 2020]] ).

The adjustment contribution to the CMIP6 ensemble mean ERFaci is –0.20 W m <sup>–2</sup> , though with considerable differences between the models ( [[#Smith--2020b|Smith et al., 2020b]] ). Generally, this adjustment in ESMs arises mainly from LWP changes (e.g., [[#Ghan--2016|Ghan et al., 2016]] ), while satellite observations suggest that cloud cover adjustments dominate and that aerosol effects on LWP are overestimated in ESMs ( [[#Bender--2019|Bender et al., 2019]] ). Large-eddy-simulations also tend to suggest an overestimated aerosol effect on cloud lifetime in ESMs, but some report an aerosol-induced decrease in cloud cover that is at odds with satellite observations ( [[#Seifert--2015|Seifert et al., 2015]] ). Despite this potential disagreement when it comes to the dominant adjustment mechanism, a substantial negative contribution to ERFaci from adjustments is supported both by observational and modelling studies.

Contributions to ERFaci from anthropogenic aerosols acting as INPs are generally not included in CMIP6 models. Two global modelling studies incorporating parametrizations based on recent laboratory studies both found a negative contribution to ERFaci ( [[#Penner--2018|Penner et al., 2018]] ; [[#McGraw--2020|McGraw et al., 2020]] ), with central estimates of –0.3 and –0.13 W m <sup>–2</sup> , respectively. However, previous studies have produced model estimates of opposing signs ( [[#Storelvmo--2017|Storelvmo, 2017]] ). There is thus ''limited evidenc'' e and ''medium agreement'' for a small negative contribution to ERFaci from anthropogenic INP-induced cirrus modifications ( ''low confidence'' ).

Similarly, aerosol effects on deep convective clouds are typically not incorporated in ESMs. However, cloud-resolving modelling studies support non-negligible aerosol effects on the radiative properties of convective clouds and associated detrained cloud anvils ( [[#Tao--2012|Tao et al., 2012]] ). While global ERF estimates are currently not available for these effects, the fact that they are missing in most ESMs adds to the uncertainty range for the model-based ERFaci.

From model-based evidence, ERFaci is assessed to –1.0 ± 0.8 W m <sup>–2</sup> ( ''medium confidence'' ). This assessment uses the mean ERFaci in Table 7.6 as a starting point, but further allows for a small negative ERF contribution from cirrus clouds. The uncertainty range is based on those reported in Table 7.6, but widened to account for uncertain but ''likely'' non-negligible processes currently unaccounted for in ESMs.

<div id="7.3.3.2.3" class="h4-container"></div>

<span id="overall-assessment-of-erfaci"></span>
===== 7.3.3.2.3 Overall assessment of ERFaci =====

<div id="h4-6-siblings" class="h4-siblings"></div>

The assessment of ERFaci based on observational evidence alone (–1.0 ± 0.7 W m <sup>–2</sup> ) is very similar to the one based on model evidence alone (–1.0 ± 0.8 W m <sup>–2</sup> ), in strong contrast to what was reported in AR5. This reconciliation of observation-based and model-based estimates is the result of considerable scientific progress and reflects comparable revisions of both model-based and observation-based estimates. The strong agreement between the two largely independent lines of evidence increases confidence in the overall assessment of the central estimate and ''very likely'' range for ERFaci of –1.0 ± 0.7 W m <sup>–2</sup> ( ''medium confidence'' ). The assessed range is consistent with but narrower than that reported by the review of [[#Bellouin--2020|Bellouin et al. (2020)]] of –2.65 to –0.07 W m <sup>–2</sup> . The difference is primarily due to a wider range in the adjustment contribution to ERFaci in [[#Bellouin--2020|Bellouin et al. (2020)]] , however adjustments reported relative to IRFaci ranging from 40% to 150% in that study are fully consistent with the ERFaci assessment presented here.

<div id="7.3.3.3" class="h3-container"></div>

<span id="energy-budget-constraints-on-the-total-aerosol-erf"></span>
==== 7.3.3.3 Energy Budget Constraints on the Total Aerosol ERF ====

<div id="h3-13-siblings" class="h3-siblings"></div>

Energy balance models of reduced complexity have in recent years increasingly been combined with Monte Carlo approaches to provide valuable ‘top-down’ (also called inverse) observational constraints on the total aerosol ERF. These top-down approaches report ranges of aerosol ERF that are found to be consistent with the global mean temperature record and, in some cases, also observed ocean heat uptake. However, the total aerosol ERF is also used together with the historical temperature record in ( [[#7.5|Section 7.5]] to constrain equilibrium climate sensitivity (ECS) and transient climate response (TCR). Using top-down estimates as a separate line of evidence also for the total aerosol ERF would therefore be circular. Nevertheless, it is useful to examine the development of these estimates since AR5, and the degree to which these estimates are consistent with the upper and lower bounds of the assessments of total aerosol ERF (ERFari+aci).

When the first top-down estimates emerged (e.g., [[#Knutti--2002|Knutti et al., 2002]] ), it became clear that some of the early (‘bottom-up’) ESM estimates of total aerosol ERF were inconsistent with the plausible top-down range. However, as more inverse estimates have been published, it has increasingly become clear that they too are model-dependent and span a wide range of ERF estimates, with confidence intervals that in some cases do not overlap ( [[#Forest--2018|Forest, 2018]] ). It has also become evident that these methods are sensitive to revised estimates of other forcings and/or updates to observational datasets. A recent review of 19 such estimates reported a mean of –0.77 W m <sup>–2</sup> for the total aerosol ERF, and a 95% confidence interval of [–1.15 to –0.31] W m <sup>–2</sup> ( [[#Forest--2018|Forest, 2018]] ). Adding to that review, a more recent study using the same approach reported an estimate of total aerosol ERF of –0.89 [–1.82 to –0.01] W m <sup>–2</sup> ( [[#Skeie--2018|Skeie et al., 2018]] ). However, in the same study, an alternative way of incorporating ocean heat content in the analysis produced a total aerosol ERF estimate of –1.34 [–2.20 to –0.46] W m <sup>–2</sup> , illustrating the sensitivity to the manner in which observations are included. A new approach to inverse estimates took advantage of independent climate radiative response estimates from eight prescribed SST and sea ice-concentration simulations over the historical period to estimate the total anthropogenic ERF. From this a total aerosol ERF of –0.8 [–1.6 to +0.1] W m <sup>–2</sup> was derived (valid for near-present relative to the late 19th century). This range was found to be more invariant to parameter choices than earlier inverse approaches ( [[#Andrews--2020|Andrews and Forster, 2020]] ).

Beyond the inverse estimates described above, other efforts have been made since AR5 to constrain the total aerosol ERF. For example, [[#Stevens--2015|Stevens (2015)]] used a simple (one-dimensional) model to simulate the historical total aerosol ERF evolution consistent with the observed temperature record. Given the lack of temporally extensive cooling trends in the 20th-century record and the fact that the historical evolution of GHG forcing is relatively well constrained, the study concluded that a more negative total aerosol ERF than –1.0 W m <sup>–2</sup> was incompatible with the historical temperature record. This was countered by [[#Kretzschmar--2017|Kretzschmar et al. (2017)]] , who argued that the model employed in [[#Stevens--2015|Stevens (2015)]] was too simplistic to account for the effect of geographical redistributions of aerosol emissions over time. Following the logic of [[#Stevens--2015|Stevens (2015)]] , but basing their estimates on a subset of CMIP5 models as opposed to a simplified modelling framework, Kretzschmar et al. argued that a total aerosol ERF as negative as –1.6 W m <sup>–2</sup> was consistent with the observed temperature record. Similar arguments were put forward by [[#Booth--2018|Booth et al. (2018)]] , who emphasized that the degree of non-linearity of the total aerosol ERF with aerosol emissions is a central assumption in [[#Stevens--2015|Stevens (2015)]] .

The historical temperature record was also the key observational constraint applied in two additional studies ( [[#Rotstayn--2015|Rotstayn et al., 2015]] ; [[#Shindell--2015|Shindell et al., 2015]] ) based on a subset of CMIP5 models. [[#Rotstayn--2015|Rotstayn et al. (2015)]] found a strong temporal correlation (&gt;0.9) between the total aerosol ERF and the global surface temperature. They used this relationship to produce a best estimate for the total aerosol ERF of –0.97 W m <sup>–2</sup> , but with considerable unquantified uncertainty, in part due to uncertainties in the TCR. [[#Shindell--2015|Shindell et al. (2015)]] came to a similar best estimate for the total aerosol ERF of –1.0 W m <sup>–2</sup> and a 95% confidence interval of –1.4 to –0.6 W m <sup>–2</sup> but based this on spatial temperature and ERF patterns in the models in comparison with observed spatial temperature patterns.

A separate observational constraint on the total ERF was proposed by [[#Cherian--2014|Cherian et al. (2014)]] , who compared trends in downward fluxes of solar radiation observed at surface stations across Europe (described in ( [[#7.2.2.3|Section 7.2.2.3]] ) to those simulated by a subset of CMIP5 models. Based on the relationship between solar radiation trends and the total aerosol ERF in the models, they inferred a total aerosol ERF of –1.3 W m <sup>–2</sup> and a standard deviation of ± 0.4 W m <sup>–2</sup> .

Based solely on energy balance considerations or other observational constraints, it is ''extremely likely'' that the total aerosol ERF is negative ( ''high confidence'' ), but ''extremely unlikely'' that the total aerosol ERF is more negative than –2.0 W m <sup>–2</sup> ( ''high confidence'' ).

<div id="7.3.3.4" class="h3-container"></div>

<span id="overall-assessment-of-total-aerosol-erf"></span>
==== 7.3.3.4 Overall Assessment of Total Aerosol ERF ====

<div id="h3-14-siblings" class="h3-siblings"></div>

In AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ), the overall assessment of total aerosol ERF (ERFari+aci) used the median of all ESM estimates published prior to AR5 of –1.5 [–2.4 to –0.6] W m <sup>–2</sup> as a starting point, but placed more confidence in a subset of models that were deemed more complete in their representation of aerosol–cloud interactions. These models, which included aerosol effects on mixed-phase, ice and/or convective clouds, produced a smaller estimate of –1.38 W m <sup>–2</sup> . Likewise, studies that constrained models with satellite observations (five in total), which produced a median estimate of –0.85 W m <sup>–2</sup> , were given extra weight. Furthermore, a longwave ERFaci of 0.2 W m <sup>–2</sup> was added to studies that only reported shortwave ERFaci values. Finally, based on higher resolution models, doubt was raised regarding the ability of ESMs to represent the cloud-adjustment component of ERFaci with fidelity. The expert judgement was therefore that aerosol effects on cloud lifetime were too strong in the ESMs, further reducing the overall ERF estimate. The above lines of argument resulted in a total aerosol assessment of –0.9 [–1.9 to –0.1] W m <sup>–2</sup> in AR5.

Here, the best estimate and range is revised relative to AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ), partly based on updates to the above lines of argument. Firstly, the studies that included aerosol effects on mixed-phase clouds in AR5 relied on the assumption that anthropogenic black carbon (BC) could act as INPs in these clouds, which has since been challenged by laboratory experiments ( [[#Kanji--2017|Kanji et al., 2017]] ; [[#Vergara-Temprado--2018|Vergara-Temprado et al., 2018]] ). There is no observational evidence of appreciable ERFs associated with aerosol effects on mixed-phase and ice clouds ( [[#7.3.3.2.1|Section 7.3.3.2.1]] ), and modelling studies disagree when it comes to both their magnitude and sign ( [[#7.3.3.2.2|Section 7.3.3.2.2]] ). Likewise, very few ESMs incorporate aerosol effects on deep convective clouds, and cloud-resolving modelling studies report different effects on cloud radiative properties depending on environmental conditions ( [[#Tao--2012|Tao et al., 2012]] ). Thus, it is not clear whether omitting such effects from ESMs would lead to any appreciable ERF biases, or if so, what the sign of such biases would be. As a result, all ESMs are given equal weight in this assessment. Furthermore, there is now a considerably expanded body of literature which suggests that early modelling studies that incorporated satellite observations may have resulted in overly conservative estimates of the magnitude of ERFaci ( [[#7.3.3.2.1|Section 7.3.3.2.1]] ). Finally, based on an assessment of the longwave ERFaci in the CMIP5 models, the offset of +0.2 W m <sup>–2</sup> applied in AR5 appears to be too large ( [[#Heyn--2017|Heyn et al., 2017]] ). As in AR5, there is still reason to question the ability of ESMs to simulate adjustments in LWP and cloud cover in response to aerosol perturbation, but it is not clear that this will result in biases that exclusively increase the magnitude of the total aerosol ERf ( [[#7.3.3.2.2|Section 7.3.3.2.2]] ).

The assessment of total aerosol ERF here uses the following lines of evidence: satellite-based evidence for IRFari; model-based evidence for IRFari and ERFari; satellite-based evidence of IRFaci and ERFaci; and finally model-based evidence for ERFaci. Based on this, ERFari and ERFaci for 2014 relative to 1750 are assessed to be –0.3 ± 0.3 W m <sup>–2</sup> and –1.0 ± 0.7 W m <sup>–2</sup> , respectively. There is thus strong evidence for a substantive negative total aerosol ERF, which is supported by the broad agreement between observation-based and model-based lines of evidence for both ERFari and ERFaci that has emerged since AR5 ( [[#Gryspeerdt--2020|Gryspeerdt et al., 2020]] ). However, considerable uncertainty remains, particularly with regards to the adjustment contribution to ERFaci, as well as missing processes in current ESMs, notably aerosol effects on mixed-phase, ice and convective clouds. This leads to a ''medium confidence'' in the estimate of ERFari+aci and a slight narrowing of the uncertainty range. Because the estimates informing the different lines of evidence are generally valid for approximately 2014 conditions, the total aerosol ERF assessment is considered valid for 2014 relative to 1750.

Combining the lines of evidence and adding uncertainties in quadrature, the ERFari+aci estimated for 2014 relative to 1750 is assessed to be –1.3 [–2.0 to –0.6] W m <sup>–2</sup> ( ''medium confidence'' ) ''.'' The corresponding range from Bellouin et al. (2019) is –3.15 to –0.35 W m <sup>–2</sup> , thus there is agreement for the upper bound while the lower bound assessed here is less negative. A lower bound more negative than –2.0 W m <sup>–2</sup> is not supported by any of the assessed lines of evidence. There is ''high confidence'' that ERFaci contributes most (75–80%) to the total aerosol effect (ERFari+aci). In contrast to AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ), it is now ''virtually certain'' that the total aerosol ERF is negative. Figure 7.5 depicts the aerosol ERFs from the different lines of evidence along with the overall assessments.

<div id="_idContainer029" class="Basic-Text-Frame"></div>

[[File:0c87fc40a5bd234b7f89a8f0e96755a5 IPCC_AR6_WGI_Figure_7_5.png]]

'''Figure 7.5''' '''|''' '''Net aerosol effective radiative forcing (ERF) from different lines of evidence.''' The headline AR6 assessment of –1.3 [–2.0 to –0.6] W m <sup>–2</sup> is highlighted in purple for 1750–2014 and compared to the AR5 assessment of –0.9 [–1.9 to –0.1] W m <sup>–2</sup> for 1750–2011. The evidence comprising the AR6 assessment is shown below this: energy balance constraints [–2 to 0 W m <sup>–2</sup> with no best estimate]; observational evidence from satellite retrievals of –1.4 [–2.2 to –0.6] W m <sup>–2</sup> ; and climate model-based evidence of –1.25 [–2.1 to –0.4] W m <sup>–2</sup> . Estimates from individual CMIP5 ( [[#Zelinka--2014|Zelinka et al., 2014]] ) and CMIP6 ( [[#Smith--2020b|Smith et al., 2020b]] and Table 7.6) models are depicted by blue and red crosses respectively. For each line of evidence the assessed best-estimate contributions from ERFari and ERFaci are shown with darker and paler shading respectively. The observational assessment for ERFari is taken from the IRFari. Uncertainty ranges are represented by black bars for the total aerosol ERF and depict ''very likely'' ranges. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

As most modelling and observational estimates of aerosol ERF have end points in 2014 or earlier, there is '''limited evidence''' available for the assessment of how aerosol ERF has changed from 2014 to 2019. However, based on a general reduction in global mean AOD over this period ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.6|Section 2.2.6]] and Figure 2.9), combined with a reduction in emissions of aerosols and their precursors in updated emissions inventories ( [[#Hoesly--2018|Hoesly et al., 2018]] ), the aerosol ERF is assessed to have decreased in magnitude from about 2014 to 2019 ( ''medium confidence'' ). Consistent with Figure 2.10, the change in aerosol ERF from about 2014 to 2019 is assessed to be +0.2 W m <sup>–2</sup> , but with ''low confidence'' due to '''limited evidence''' . Aerosols are therefore assessed to have contributed an ERF of –1.1 [–1.7 to –0.4] W m <sup>–2</sup> over 1750–2019 ( ''medium confidence'' ).

<div id="7.3.4" class="h2-container"></div>

<span id="other-agents"></span>