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

===== 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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