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

==== 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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