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

== 7.3 Effective Radiative Forcing ==

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Effective radiative forcing (ERF) quantifies the energy gained or lost by the Earth system following an imposed perturbation (for instance in GHGs, aerosols or solar irradiance). As such it is a fundamental driver of changes in the Earth’s TOA energy budget. ERF is determined by the change in the net downward radiative flux at the TOA (Box 7.1) after the system has adjusted to the perturbation but excluding the radiative response to changes in surface temperature. This section outlines the methodology for ERF calculations ( [[#7.3.1|Section 7.3.1]] ) and then assesses the ERF due to greenhouse gases ( [[#7.3.2|Section 7.3.2]] ), aerosols ( [[#7.3.3|Section 7.3.3]] ) and other natural and anthropogenic forcing agents ( [[#7.3.4|Section 7.3.4]] ). These are brought together in ( [[#7.3.5|Section 7.3.5]] for an overall assessment of the present-day ERF and its evolution over the historical time period from 1750 to 2019. The same section also evaluates the surface temperature response to individual ERFs.

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=== 7.3.1 Methodologies and Representation in Models: Overview of Adjustments ===

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As introduced in Box 7.1, AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ; [[#Myhre--2013b|Myhre et al., 2013b]] ) recommended ERF as a more useful measure of the climate effects of a physical driver than the stratospheric-temperature-adjusted radiative forcing (SARF) adopted in earlier assessments. The AR5 assessed that the ratios of surface temperature change to forcing resulting from perturbations of different forcing agents were more similar between species using ERF than SARF. ERF extended the SARF concept to account for not only adjustments to stratospheric temperatures, but also responses in the troposphere and effects on clouds and atmospheric circulation, referred to as ‘adjustments’. For more details see Box 7.1. Since circulation can be affected, these responses are not confined to the locality of the initial perturbation (unlike the traditional stratospheric-temperature adjustment).

This chapter defines ‘adjustments’ as those changes caused by the forcing agent that are independent of changes in surface temperature, rather than defining a specific time scale. The AR5 used the term ‘rapid adjustment’, but in this assessment the definition is based on the independence from surface temperature rather than the rapidity. The definition of ERF in Box 7.1 aims to create a clean separation between forcing (energy budget changes that are not mediated by surface temperature) and feedbacks (energy budget changes that are mediated by surface temperature). This means that changes in land or ocean surface temperature patterns (for instance as identified by [[#Rugenstein--2016b|Rugenstein et al., 2016b]] ) are not included as adjustments. As in previous assessments ( [[#Forster--2007|Forster et al., 2007]] ; [[#Myhre--2013b|Myhre et al., 2013b]] ) ERFs can be attributed simply to changes in the forcing agent itself or attributed to components of emitted gases (Figure 6.12). Because ERFs can include chemical and biospheric responses to emitted gases, they can be attributed to precursor gases, even if those gases do not have a direct radiative effect themselves. Similar chemical and biospheric responses to forcing agents can also be included in the ERF in addition to their direct effects.

Instantaneous radiative forcing (IRF) is defined here as the change in the net TOA radiative flux following a perturbation, excluding any adjustments. SARF is defined here as the change in the net radiative flux at TOA following a perturbation including the response to stratospheric temperature adjustments. These differ from AR5 where these quantities were defined at the tropopause ( [[#Myhre--2013b|Myhre et al., 2013b]] ). The net IRF values will be different using the TOA definition. The net SARF values will be the same as with the tropopause definition, but will have a different partitioning between the longwave and shortwave. Defining all quantities at the TOA enables consistency in breaking down the ERF into its component parts.

The assessment of ERFs in AR5 was preliminary because ERFs were only available for a few forcing agents, so for many forcing agents the Report made the assumption that ERF and SARF were equivalent. This section discusses the body of work published since AR5. This work has computed ERFs across many more forcing agents and models; closely examined the methods of computation; quantified the processes involved in causing adjustments; and examined how well ERFs predict the ultimate temperature response. This work is assessed to have led to a much-improved understanding and increased confidence in the quantification of radiative forcing across the Report. These same techniques allow for an evaluation of radiative forcing within Earth system models (ESMs) as a key test of their ability to represent both historical and future temperature changes (Sections 3.3.1 and 4.3.4).

The ERF for a particular forcing agent is the sum of the IRF and the contribution from the adjustments, so in principle this could be constructed bottom-up by calculating the IRF and adding in the adjustment contributions one-by-one or together. However, there is no simple way to derive the global tropospheric adjustment terms or adjustments related to circulation changes without using a comprehensive climate model (e.g., CMIP5 or CMIP6). There have been two main modelling approaches used to approximate the ERF definition in Box 7.1. The first approach is to use the assumed linearity (Box 7.1, Equation 7.1) to regress the net change in the TOA radiation budget (Δ ''N'' ) against change in global mean surface temperature (Δ ''T'' ) following a step change in the forcing agent (Box 7.1, Figure 1; [[#Gregory--2004|Gregory et al., 2004]] ). The ERF (Δ ''F'' ) is then derived from Δ ''N'' when Δ ''T'' = 0. Regression-based estimates of ERF depend on the temporal resolution of the data used ( [[#Modak--2016|Modak et al., 2016]] , 2018). For the first few months of a simulation both surface temperature change and stratospheric-temperature adjustment occur at the same time, leading to misattribution of the stratospheric-temperature adjustment to the surface temperature feedback. Patterns of sea surface temperature (SST) change also affect estimates of the forcing obtained by regression methods ( [[#Andrews--2015|Andrews et al., 2015]] ). At multi-decadal time scales the curvature of the relationship between net TOA radiation and surface temperature can also lead to biases in the ERF estimated from the regression method ( [[#7.4|Section 7.4]] ; [[#Armour--2013|Armour et al., 2013]] ; [[#Andrews--2015|Andrews et al., 2015]] ; [[#Knutti--2017|Knutti et al., 2017]] ). The second modelling approach to estimate ERF is to set the Δ ''T'' term in Box 7.1 (Box 7.1, Equation 7.1) to zero. It is technically difficult to constrain land surface temperatures in ESMs ( [[#Shine--2003|Shine et al., 2003]] ; [[#Ackerley--2016|Ackerley and Dommenget, 2016]] ; [[#Andrews--2021|Andrews et al., 2021]] ), so most studies reduce the Δ ''T'' term by prescribing the SSTs and sea ice concentrations in a pair of ‘fixed-SST’ (fSST) simulations with and without the change in forcing agent ( [[#Hansen--2005b|Hansen et al., 2005b]] ). An approximation to ERF (Δ ''F'' <sub>fsst</sub> ) is then given by the difference in Δ ''N'' <sub>fsst 4</sub> between the simulations. The fSST method has less noise due to internal variability than the regression method. Nevertheless a 30-year fSST integration or 10 × 20-year regression ensemble needs to be conducted in order to reduce the 5–95% confidence range to 0.1 W m <sup>–2</sup> ( [[#Forster--2016|Forster et al., 2016]] ).Neither the regression or fSST methods are practical for quantifying the ERF of agents with forcing magnitudes of the order of 0.1 W m <sup>–2</sup> or smaller. The internal variability in the fSST method can be further constrained by nudging winds towards a prescribed climatology ( [[#Kooperman--2012|Kooperman et al., 2012]] ). This allows the determination of the ERF of forcing agents with smaller magnitudes but excludes adjustments associated with circulation responses ( [[#Schmidt--2018|Schmidt et al., 2018]] ). There are insufficient studies to assess whether these circulation adjustments are significant.

Since the near-surface temperature change over land, Δ ''T'' <sub>land</sub> , is not constrained in the fSST method, this response needs to be removed for consistency with the ( [[#7.1|Section 7.1]] definition. These changes in the near-surface temperature will also induce further responses in the tropospheric temperature and water vapour that should also be removed to conform with the physical definition of ERF. The radiative response to Δ ''T'' <sub>land</sub> can be estimated through radiative transfer modelling in which a kernel, ''k'' , representing the change in net TOA radiative flux per unit of change in near-surface temperature change over land (or an approximation using land surface temperature), is precomputed ( [[#Smith--2018b|Smith et al., 2018b]] , 2020b; [[#Richardson--2019|Richardson et al., 2019]] ; [[#Tang--2019|Tang et al., 2019]] ). Thus ERF ≈ Δ ''F'' <sub>fsst</sub> – ''k'' Δ ''T'' <sub>land</sub> . Since ''k'' is negative this means that Δ ''F'' <sub>fsst</sub> underestimates the ERF. For 2×CO <sub>2</sub> , this underestimate is around 0.2 W m <sup>–2</sup> ( [[#Smith--2018b|Smith et al., 2018b]] , 2020b). There have been estimates of the corrections due to tropospheric temperature and water vapour ( [[#Tang--2019|Tang et al., 2019]] ; [[#Smith--2020b|Smith et al., 2020b]] ) showing additional radiative responses of comparable magnitude to those directly from Δ ''T'' <sub>land</sub> . An alternative to computing the response terms directly is to use the feedback parameter, α ( [[#Hansen--2005b|Hansen et al., 2005b]] ; [[#Sherwood--2015|Sherwood et al., 2015]] ; [[#Tang--2019|Tang et al., 2019]] ). This gives approximately double the correction compared to the kernel approach ( [[#Tang--2019|Tang et al., 2019]] ). The response to land surface temperature change varies with location and even for GSAT change ''k'' is not expected to be the same as α [[#7.4|Section 7.4]] ). One study where land surface temperatures are constrained in a model ( [[#Andrews--2021|Andrews et al., 2021]] ) finds this constraint adds +1.0 W m <sup>–2</sup> to Δ ''F'' <sub>fsst</sub> for 4×CO <sub>2</sub> , thus confirming the need for a correction in calculations where this constraint is not applied. For this assessment the correction is conservatively based only on the direct radiative response kernel to Δ ''T'' <sub>land</sub> as this has a strong theoretical basis to support it. While there is currently insufficient corroborating evidence to recommend including tropospheric temperature and water-vapour corrections in this assessment, it is noted that the science is progressing rapidly on this topic.

TOA radiative flux changes due to the individual adjustments can be calculated by perturbing the meteorological fields in a climate model’s radiative transfer scheme (partial radiative perturbation approach) ( [[#Colman--2015|Colman, 2015]] ; [[#Mülmenstädt--2019|Mülmenstädt et al., 2019]] ) or by using precomputed radiative kernels of sensitivities of the TOA radiation fluxes to changes in these fields (as done for near-surface temperature change above; [[#Vial--2013|Vial et al., 2013]] ; [[#Zelinka--2014|Zelinka et al., 2014]] ; [[#Zhang--2014|Zhang and Huang, 2014]] ; [[#Smith--2018b|Smith et al., 2018b]] , 2020b). The radiative kernel approach is easier to implement through post-processing of output from multiple ESMs, whereas it is recognized that the partial radiation perturbation approach gives a more accurate estimate of the adjustments within the setup of a single model and its own radiative transfer code. There is little difference between using a radiative kernel from the same or a different model when calculating the adjustment terms, except for stratospheric temperature adjustments where it is important to have sufficient vertical resolution in the stratosphere in the model used to derive the kernel ( [[#Smith--2018b|Smith et al., 2018b]] , 2020a).

For comparison with offline radiative transfer calculations the SARFs can be approximated by removing the adjustment terms (apart from stratospheric temperature) from the ERFs using radiative kernels to quantify the adjustment for each meteorological variable. Kernel analysis by [[#Chung--2015|Chung and Soden (2015)]] suggested a large spread in CO <sub>2</sub> SARF across climate models, but their analysis was based on regressing variables in a coupled-ocean experiment rather than using a fSST approach which leads to a large spread due to natural variability ( [[#Forster--2016|Forster et al., 2016]] ). Adjustments computed from radiative kernels are shown for seven different climate drivers (using a fSST approach) in Figure 7.4. Table 7.2 shows the estimates of SARF, Δ ''F'' <sub>fsst</sub> and ERF (corrected for land surface temperature change) for 2×CO <sub>2</sub> from the nine climate models analysed in [[#Smith--2018b|Smith et al. (2018b)]] . The SARF shows a smaller spread over previous studies ( [[#Pincus--2016|Pincus et al., 2016]] ; [[#Soden--2018|Soden et al., 2018]] ) and most estimates are within 10% of the multi-model mean and the assessment of 2×CO <sub>2</sub> SARF in ( [[#7.3.2|Section 7.3.2]] (3.75 W m <sup>–2</sup> ). It is not possible from these studies to determine how much of this reduction in spread is due to convergence in the model radiation schemes or the meteorological conditions of the model base states; nevertheless the level of agreement in this and earlier intercomparisons gives ''medium confidence'' in the ability of ESMs to represent radiative forcing from CO <sub>2</sub> . The 4×CO <sub>2</sub> CMIP6 fSST experiments ( [[#Smith--2020b|Smith et al., 2020b]] ) in Table 7.2 include ESMs with varying levels of complexity in aerosols and reactive gas chemistry. The CMIP6 experimental setup allows for further climate effects of CO <sub>2</sub> (including on aerosols and ozone) depending on model complexity. The chemical effects are adjustments to CO <sub>2</sub> but are not separable from the SARF in the diagnosis in Table 7.2. In these particular models, this leads to higher SARF than when only CO <sub>2</sub> varies, however there are insufficient studies to make a formal assessment of composition adjustments to CO <sub>2</sub> .

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[[File:71c76ec9643e9fe81cead6cedb433f25 IPCC_AR6_WGI_Figure_7_4.png]]

'''Figure 7.4''' '''|''' '''Radiative adjustments at top of atmosphere for seven different climate drivers as a proportion of forcing.''' Tropospheric temperature (orange), stratospheric temperature (yellow), water vapour (blue), surface albedo (green), clouds (grey) and the total adjustment (black) is shown. For the greenhouse gases (carbon dioxide, methane, nitrous oxide and CFC-12) the adjustments are expressed as a percentage of stratospheric-temperature-adjusted radiative forcing (SARF), whereas for aerosol, solar and volcanic forcing they are expressed as a percentage of instantaneous radiative forcing (IRF). Land surface temperature response (outline red bar) is shown, but included in the definition of forcing. Data from [[#Smith--2018b|Smith et al. (2018b)]] for carbon dioxide and methane; [[#Smith--2018b|Smith et al. (2018b)]] and [[#Gray--2009|Gray et al. (2009)]] for solar; [[#Hodnebrog--2020b|Hodnebrog et al. (2020b)]] for nitrous oxide and CFC-12; [[#Smith--2020b|Smith et al. (2020b)]] for aerosol, and [[#Marshall--2020|Marshall et al. (2020)]] for volcanic. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

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'''Table 7.2''' '''|''' '''SARF, Δ''' ''F'' <sub>fsst</sub> ''', and ERF diagnosed from Earth system models for fixed-SST (fSST) CO''' <sub>2</sub> '''experiments.''' 2×CO <sub>2</sub> data taken from fixed atmospheric composition experiments ( [[#Smith--2018b|Smith et al., 2018b]] ). 4×CO <sub>2</sub> data taken from CMIP6 experiments with interactive aerosols (and interactive gas phase chemistry in some; [[#Smith--2020b|Smith et al., 2020b]] ). The radiative forcings from the 4×CO <sub>2</sub> experiments are scaled by 0.476 for comparison with 2×CO <sub>2</sub> ( [[#Meinshausen--2020|Meinshausen et al., 2020]] ). SARF is approximated by removing the (non-stratospheric-temperature) adjustment terms from the ERF. In [[#Smith--2018b|Smith et al. (2018b)]] , separation of temperature adjustments into tropospheric and stratospheric contributions is approximate based on a fixed tropopause of 100 hPa at the equator, varying linearly in latitude to 300 hPa at the poles. In [[#Smith--2020b|Smith et al. (2020b)]] , this separation is based on the model-diagnosed tropopause. ERF is approximated by removing the response to land surface temperature change from Δ ''F'' <sub>fsst</sub> . The confidence range is based on the inter-model standard deviation.

{| class="wikitable"
|-
| 2×CO <sub>2</sub> Experiments

( [[#Smith--2018b|Smith et al., 2018b]] )

| Stratospheric- temperature-adjusted Radiative Forcing (SARF, W m <sup>–2</sup> )

| Δ ''F'' <sub>fsst</sub> (W m <sup>–2</sup> )

| Effective Radiative Forcing (ERF, W m <sup>–2</sup> )

|-
| HadGEM2-ES

| 3.45

| 3.37

| 3.58

|-
| NorESM1

| 3.67

| 3.50

| 3.70

|-
| GISS-E2-R

| 3.98

| 4.06

| 4.27

|-
| CanESM2

| 3.68

| 3.57

| 3.77

|-
| MIROC-SPRINTARS

| 3.89

| 3.62

| 3.82

|-
| NCAR-CESM1-CAM5

| 3.89

| 4.08

| 4.39

|-
| HadGEM3

| 3.48

| 3.64

| 3.90

|-
| IPSL-CM5A

| 3.50

| 3.39

| 3.61

|-
| MPI-ESM

| 4.27

| 4.14

| 4.38

|-
| NCAR-CESM1-CAM4

| 3.50

| 3.62

| 3.86

|-
| Multi-model mean and 5–95% confidence range

| 3.73 ± 0.44

| 3.70 ± 0.44

| 3.93 ± 0.48

|-
|
|-
| 0.476 × 4×CO <sub>2</sub> Experiments

( [[#Smith--2020b|Smith et al., 2020b]] )

| Stratospheric- temperature-adjusted Radiative Forcing (SARF, W m <sup>–2</sup> )

| Δ ''F'' <sub>fsst</sub> (W m <sup>–2</sup> )

| Effective Radiative Forcing (ERF, W m <sup>–2</sup> )

|-
| ACCESS-CM2

| 3.56

| 3.78

| 3.98

|-
| CanESM5

| 3.67

| 3.62

| 3.82

|-
| CESM2

| 3.56

| 4.24

| 4.48

|-
| CNRM-CM6-1

| 3.99

| 3.81

| 4.01

|-
| CNRM-ESM2-1

| 3.99

| 3.77

| 3.94

|-
| EC-Earth3

|
| 3.85

| 4.04

|-
| GFDL-CM4

| 3.65

| 3.92

| 4.10

|-
| GFDL-ESM4

| 3.27

| 3.68

| 3.85

|-
| GISS-E2-1-G

| 3.78

| 3.50

| 3.69

|-
| HadGEM3-GC31-LL

| 3.61

| 3.85

| 4.07

|-
| IPSL-CM6A-LR

| 3.84

| 3.81

| 4.05

|-
| MIROC6

| 3.63

| 3.48

| 3.69

|-
| MPI-ESM1-2-LR

| 3.74

| 3.97

| 4.20

|-
| MRI-ESM2-0

| 3.76

| 3.64

| 3.80

|-
| NorESM2-LM

| 3.58

| 3.88

| 4.10

|-
| NorESM2-MM

| 3.62

| 3.99

| 4.22

|-
| UKESM1-0-LL

| 3.49

| 3.78

| 4.01

|-
| Multi-model mean and 5–95% confidence range

| 3.67 ± 0.29

| 3.80 ± 0.30

| 4.00 ± 0.32

|}

ERFs have been found to yield more consistent values of GSAT change per unit forcing than SARF, that is, α shows less variation across different forcing agents ( [[#Rotstayn--2001|Rotstayn and Penner, 2001]] ; [[#Shine--2003|Shine et al., 2003]] ; [[#Hansen--2005b|Hansen et al., 2005b]] ; [[#Marvel--2016|Marvel et al., 2016]] ; [[#Richardson--2019|Richardson et al., 2019]] ). Having a consistent relationship between forcing and response is advantageous when making climate projections using simple models (Cross-Chapter Box 7.1) or emissions metrics ( [[#7.6|Section 7.6]] ). The definition of ERF used in this assessment, which excludes the radiative response to land surface temperature changes, brings The α values into closer agreement than when SARF is used ( [[#Richardson--2019|Richardson et al., 2019]] ), although for individual models there are still variations, particularly for more geographically localized forcing agents. However, even for ERF, studies find that α is not identical across all forcing agents ( [[#Shindell--2014|Shindell, 2014]] ; [[#Shindell--2015|Shindell et al., 2015]] ; [[#Modak--2018|Modak et al., 2018]] ; [[#Modak--2019|Modak and Bala, 2019]] ; [[#Richardson--2019|Richardson et al., 2019]] ). [[#7.4.4|Section 7.4.4]] discusses the effect of different SST response patterns on α . Analysis of the climate feedbacks ( [[#Kang--2014|Kang and Xie, 2014]] ; [[#Gregory--2016|Gregory et al., 2016]] , 2020; [[#Marvel--2016|Marvel et al., 2016]] ; [[#Duan--2018|Duan et al., 2018]] ; [[#Persad--2018|Persad and Caldeira, 2018]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ; [[#Krishnamohan--2019|Krishnamohan et al., 2019]] ) suggests a weaker feedback (i.e., less-negative α ) and hence larger sensitivity for forcing of the higher latitudes (particularly the Northern Hemisphere). Nonetheless, as none of these variations are robust across models, the ratio of 1/ α from non-CO <sub>2</sub> forcing agents (with approximately global distributions) to that from doubling CO <sub>2</sub> is within 10% of unity.

In summary, this Report adopts an estimate of ERF based on the change in TOA radiative fluxes in the absence of GSAT changes. This allows for a theoretically cleaner separation between forcing and feedbacks in terms of factors respectively unrelated and related to GSAT change (Box 7.1). ERF can be computed from prescribed SST and sea ice experiments after removing the TOA energy budget change associated with the land surface temperature response. In this assessment this is removed using a kernel accounting only for the direct radiative effect of the land surface temperature response. To compare these results with sophisticated high spectral resolution radiative transfer models the individual tropospheric adjustment terms can be removed to leave the SARF. SARFs for 2×CO <sub>2</sub> calculated by ESMs from this method agree within 10% with the more sophisticated models. The new studies highlighted above suggest that physical feedback parameters computed within this framework have less variation across forcing agents. There is ''high confidence'' that an α based on ERF as defined here varies by less (less than variation 10% across a range of forcing agents with global distributions), than α based on SARF. For geographically localized forcing agents there are fewer studies and less agreement between them, resulting in ''low confidence'' that ERF is a suitable estimator of the resulting global mean near-surface temperature response ''.''

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=== 7.3.2 Greenhouse Gases ===

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High spectral resolution radiative transfer models provide the most accurate calculations of radiative perturbations due to greenhouse gases (GHGs), with errors in the instantaneous radiative forcing (IRF) of less than 1% ( [[#Mlynczak--2016|Mlynczak et al., 2016]] ; [[#Pincus--2020|Pincus et al., 2020]] ). They can calculate IRFs with no adjustments, or SARFs by accounting for the adjustment of stratospheric temperatures using a fixed dynamical heating. It is not possible with offline radiation models to account for other adjustments. The high-resolution model calculations of SARF for carbon dioxide, methane and nitrous oxide have been updated since AR5, which were based on [[#Myhre--1998|Myhre et al. (1998)]] . The new calculations include the shortwave forcing from methane and updates to the water vapour continuum (increasing the total SARF of methane by 25%) and account for the absorption band overlaps between carbon dioxide and nitrous oxide ( [[#Etminan--2016|Etminan et al., 2016]] ). The associated simplified expressions, from a re-fitting of the [[#Etminan--2016|Etminan et al. (2016)]] results by [[#Meinshausen--2020|Meinshausen et al. (2020)]] , are given in Supplementary Material, Table 7.SM.1. The shortwave contribution to the IRF of methane has been confirmed independently ( [[#Collins--2018|Collins et al., 2018]] ). Since they incorporate known missing effects we assess the new calculations as being a more appropriate representation than [[#Myhre--1998|Myhre et al. (1998)]] .

As described in ( [[#7.3.1|Section 7.3.1]] , ERFs can be estimated using ESMs, however the radiation schemes in climate models are approximations to high spectral resolution radiative transfer models with variations and biases in results between the schemes ( [[#Pincus--2015|Pincus et al., 2015]] ). Hence ESMs alone are not sufficient to establish ERF best estimates for the well-mixed GHGs (WMGHGs). This assessment therefore estimates ERFs from a combined approach that uses the SARF from radiative transfer models and adds the tropospheric adjustments derived from ESMs.

In AR5, the main information used to assess components of ERFs beyond SARF was from [[#Vial--2013|Vial et al. (2013)]] who found a near-zero non-stratospheric adjustment (without correcting for near-surface temperature changes over land) in 4×CO <sub>2</sub> CMIP5 model experiments, with an uncertainty of ±10% of the total CO <sub>2</sub> ERF. No calculations were available for other WMGHGs, so ERF was therefore assessed to be approximately equal to SARF (within 10%) for all WMGHGs.

The effect of WMGHGs in ESMs can extend beyond their direct radiative effects to include effects on ozone and aerosol chemistry and natural emissions of ozone and aerosol precursors, and in the case of CO <sub>2</sub> to vegetation cover through physiological effects. In some cases these can have significant effects on the overall radiative budget changes from perturbing WMGHGs within ESMs ( [[#Myhre--2013b|Myhre et al., 2013b]] ; [[#Zarakas--2020|Zarakas et al., 2020]] ; [[#O’Connor--2021|O’Connor et al., 2021]] ; [[#Thornhill--2021a|Thornhill et al., 2021a]] ). These composition adjustments are further discussed in ( [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Section 6.4.2).

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==== 7.3.2.1 Carbon Dioxide (CO <sub>2</sub> ) ====

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The SARF for carbon dioxide (CO <sub>2</sub> ) has been slightly revised due to updates to spectroscopic data and inclusion of the absorption band overlaps between N <sub>2</sub> O and CO <sub>2</sub> ( [[#Etminan--2016|Etminan et al., 2016]] ). The formulae fitting to the [[#Etminan--2016|Etminan et al. (2016)]] results in [[#Meinshausen--2020|Meinshausen et al. (2020)]] are used. This increases the SARF due to doubling CO <sub>2</sub> slightly from 3.71 W m <sup>–2</sup> in AR5 to 3.75 W m <sup>–2</sup> . Tropospheric responses to CO <sub>2</sub> in fSST experiments have been found to lead to an approximate balance in their radiative effects between an increased radiative forcing due to water vapour, cloud and surface-albedo adjustments and a decrease due to increased tropospheric temperature and land surface temperature response (Table 7.3; [[#Vial--2013|Vial et al., 2013]] ; [[#Zhang--2014|Zhang and Huang, 2014]] ; [[#Smith--2018b|Smith et al., 2018b]] , 2020b). The Δ ''F'' <sub>fsst</sub> includes any effects represented within the ESMs on tropospheric adjustments due to changes in evapotranspiration or leaf area (mainly affecting surface and boundary-layer temperature, low-cloud amount, and albedo) from the CO <sub>2</sub> -physiological effects (Doutriaux- [[#Boucher--2009|Boucher et al., 2009]] ; [[#Cao--2010|Cao et al., 2010]] ; T.B. [[#Richardson--2018|]] [[#Richardson--2018|Richardson et al., 2018]] ). The effect on surface temperature (negative longwave response) is consistent with the expected physiological responses and needs to be removed for consistency with the ERF definition. The split between surface and tropospheric temperature responses was not reported in [[#Vial--2013|Vial et al. (2013)]] or [[#Zhang--2014|Zhang and Huang (2014)]] but the total of surface and tropospheric temperature response agrees with Smith et al. (2018b, 2020b), giving ''medium confidence'' in this decomposition. Doutriaux- [[#Boucher--2009|Boucher et al. (2009)]] and [[#Andrews--2021|Andrews et al. (2021)]] (using the same land surface model) find a 13% and 10% increase respectively in ERF due to the physiological responses to CO <sub>2</sub> . The physiological adjustments are therefore assessed to make a substantial contribution to the overall tropospheric adjustment for CO <sub>2</sub> ( ''high confidence'' ), but there is insufficient evidence to provide a quantification of the split between physiological and thermodynamic adjustments. These forcing adjustments due to the effects of CO <sub>2</sub> on plant physiology differ from the biogeophysical feedbacks due to the effects of temperature changes on vegetation discussed in ( [[#7.4.2.5|Section 7.4.2.5]] . The adjustment is assumed to scale with the SARF in the absence of evidence for non-linearity. The tropospheric adjustment is assessed from Table 7.3 to be +5% of the SARF with an uncertainty of 5%, which is added to the [[#Meinshausen--2020|Meinshausen et al. (2020)]] formula for SARF. Due to the agreement between the studies and the understanding of the physical mechanisms there is ''medium confidence'' in the mechanisms underpinning the tropospheric adjustment, but ''low confidence'' in its magnitude ''.''

The ERF from doubling CO <sub>2</sub> (2×CO <sub>2</sub> ) from the 1750 level (278 ppm; [[IPCC:Wg1:Chapter:Chapter-2#2.2.3.3|Section 2.2.3.3]] ) is assessed to be 3.93 ± 0.47 W m <sup>–2</sup> ( ''high confidence'' ). Its assessed components are given in Table 7.4. The combined spectroscopic and radiative transfer modelling uncertainties give an uncertainty in the CO <sub>2</sub> SARF of around 10% or less ( [[#Etminan--2016|Etminan et al., 2016]] ; [[#Mlynczak--2016|Mlynczak et al., 2016]] ). The overall uncertainty in CO <sub>2</sub> ERF is assessed as ±12%, as the more uncertain adjustments only account for a small fraction of the ERF (Table 7.3). The 2×CO <sub>2</sub> ERF estimate is 0.2 W m <sup>–2</sup> larger than using the AR5 formula ( [[#Myhre--2013b|Myhre et al., 2013b]] ) due to the combined effects of tropospheric adjustments which were assumed to be zero in AR5. CO <sub>2</sub> concentrations have increased from 278 ppm in 1750 to 410 ppm in 2019 [[IPCC:Wg1:Chapter:Chapter-2#2.2.3.3|Section 2.2.3.3]] ). The historical ERF estimate from CO <sub>2</sub> is revised upwards from the AR5 value of 1.82 ± 0.38 W m <sup>–2</sup> (1750–2011) to 2.16 ± 0.26 W m <sup>–2</sup> (1750–2019) in this assessment, from a combination of the revisions described above (0.06 W m <sup>–2</sup> ) and the 19 ppm rise in atmospheric concentrations between 2011 and 2019 (0.27 W m <sup>–2</sup> ). The ESM estimates of 2×CO <sub>2</sub> ERF (Table 7.2) lie within ±12% of the assessed value (apart from CESM2). The definition of ERF can also include further physiological effects – for instance on dust, natural fires and biogenic emissions from the land and ocean – but these are not typically included in the modelling setup for 2×CO <sub>2</sub> ERF.

<div id="_idContainer023" class="Basic-Text-Frame"></div>

'''Tabl''' '''e 7.3 |''' '''Adjustments to the top-of-atmosphere (TOA) carbon dioxide forcing due to changes in stratospheric temperature, surface and tropospheric temperatures, water vapour, clouds, and surface albedo, as a fraction of the stratospheric-temperature-adjusted radiative forcing (SARF).''' Effective radiative forcing (ERF) is defined in this Report as excluding the surface temperature response.

{| class="wikitable"
|-
| Percentage of SARF (source study)

| Surface Temperature

| Tropospheric Temperature

| Stratospheric

Temperature

| Surface Albedo

| Water Vapour

| Clouds

| Troposphere

(Including Surface)

| Troposphere

(Excluding Surface)

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

| colspan="2"| –20% combined

| N/A

| 2%

| 6%

| 11%

| –1%

| N/A

|-
| [[#Zhang--2014|Zhang and Huang (2014)]]

| colspan="2"| –23% combined

| 26%

| N/A

| 6%

| 16%

| –1%

| N/A

|-
| [[#Smith--2018b|Smith et al. (2018b)]]

| –6%

| –16%

| 30%

| 3%

| 6%

| 12%

| –1%

| +5%

|-
| [[#Smith--2020b|Smith et al. (2020b)]]

| –6%

| –15%

| 35%

| 3%

| 6%

| 15%

| +3%

| +9%

|}

<div id="_idContainer024" class="Basic-Text-Frame mt-4"></div>

'''Table 7.''' '''4 |''' '''Assessed effective radiative forcing (ERF), stratospheric-temperature-adjusted radiative forcing (SARF) and tropospheric adjustments to 2×CO''' <sub>2</sub> '''change since pre-industrial times compared to the AR5 assessed range ( [[#Myhre--2013b|Myhre et al., 2013b]] ).''' Adjustments are due to changes in tropospheric temperatures, water vapour, clouds and surface albedo and land cover and are taken from [[#Smith--2018b|Smith et al. (2018b)]] and assessed as a percentage of SARF (Table 7.3). Uncertainties are based on multi-model spread in [[#Smith--2018b|Smith et al. (2018b)]] . Note some of the uncertainties are anticorrelated, which means that they do not sum linearly.

{| class="wikitable"
|-
| 2×CO <sub>2</sub> Forcing

| AR5

SARF/ERF (W m <sup>–2</sup> )

| SARF

(W m <sup>–2</sup> )

| Tropospheric Temperature Adjustment

(W m <sup>–2</sup> )

| Water Vapour Adjustment

(W m <sup>–2</sup> )

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

| Surface Albedo and Land-cover Adjustment (W m <sup>–2</sup> )

| Total Tropospheric Adjustment (W m <sup>–2</sup> )

| ERF

(W m <sup>–2</sup> )

|-
| 2×CO <sub>2</sub> ERF components

| 3.71

| 3.75

| –0.60

| 0.22

| 0.45

| 0.11

| 0.18

| 3.93

|-
| 5–95% uncertainty ranges as percentage of ERF

| 10% (SARF)

20% (ERF)

| &lt;10%

| ±6%

| ±4%

| ±7%

| ±2%

| ±7%

| ±12%

|}

<div id="7.3.2.2" class="h3-container"></div>

<span id="methane-ch-4"></span>
==== 7.3.2.2 Methane (CH <sub>4</sub> ) ====

<div id="h3-5-siblings" class="h3-siblings"></div>

The SARF for methane (CH <sub>4</sub> ) has been substantially increased due to updates to spectroscopic data and inclusion of shortwave absorption ( [[#Etminan--2016|Etminan et al., 2016]] ). Adjustments have been calculated in nine climate models by [[#Smith--2018b|Smith et al. (2018b)]] . Since CH <sub>4</sub> is found to absorb in the shortwave near infrared, only adjustments from those models including this absorption are taken into account. For these models the adjustments act to reduce the ERF because the shortwave absorption leads to tropospheric heating and reductions in upper tropospheric cloud amounts. The adjustment is –14% ± 15%, which counteracts much of the increase in SARF identified by [[#Etminan--2016|Etminan et al. (2016)]] . [[#Modak--2018|Modak et al. (2018)]] also found negative forcing adjustments from a methane perturbation including shortwave absorption in the NCAR CAM5 model, in agreement with the above assessment. The uncertainty in the shortwave component leads to a higher radiative modelling uncertainty (14%) than for CO <sub>2</sub> ( [[#Etminan--2016|Etminan et al., 2016]] ). When combined with the uncertainty in the adjustment, this gives an overall uncertainty of ±20%. There is ''high confidence'' in the spectroscopic revision but only ''medium confidence'' in the adjustment modification. CH <sub>4</sub> concentrations have increased from 729 ppb in 1750 to 1866 ppb in 2019 [[IPCC:Wg1:Chapter:Chapter-2#2.2.3.3|Section 2.2.3.3]] ). The historical ERF estimate from AR5 of 0.48 ± 0.10 W m <sup>–2</sup> (1750–2011) is revised to 0.54 ± 0.11 W m <sup>–2</sup> (1750 to 2019) in this assessment from a combination of spectroscopic radiative efficiency revisions (+0.12 W m <sup>–2</sup> ), adjustments (–0.08 W m <sup>–2</sup> ) and the 63 ppb rise in atmospheric CH <sub>4</sub> concentrations between 2011 and 2019 (+0.03 W m <sup>–2</sup> ). As the adjustments are assessed to be small, there is ''high confidence'' in the overall assessment of ERF from methane. Increased methane leads to tropospheric ozone production and increased stratospheric water vapour, so that an attribution of forcing to methane emissions gives a larger effect than that directly from the methane concentration itself. This is discussed in detail in ( [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Section 6.4.2) and shown in Figure 6.12.

<div id="7.3.2.3" class="h3-container"></div>

<span id="nitrous-oxide-n-2-o"></span>
==== 7.3.2.3 Nitrous oxide (N <sub>2</sub> O) ====

<div id="h3-6-siblings" class="h3-siblings"></div>

The tropospheric adjustments to nitrous oxide (N <sub>2</sub> O) have been calculated from 5 ESMs as 7% ± 13% of the SARF ( [[#Hodnebrog--2020b|Hodnebrog et al., 2020b]] ). This value is therefore taken as the assessed adjustment, but with ''low confidence'' . The radiative modelling uncertainty is ±10% ( [[#Etminan--2016|Etminan et al., 2016]] ), giving an overall uncertainty of ±16%. Nitrous oxide concentrations have increased from 270 ppb in 1750 to 332 ppb in 2019 [[IPCC:Wg1:Chapter:Chapter-2#2.2.3.3|Section 2.2.3.3]] ). The historical ERF estimate from N <sub>2</sub> O is revised upwards from 0.17 ± 0.06 W m <sup>–2</sup> (1750–2011) in AR5 to 0.21 ± 0.03 W m <sup>–2</sup> (1750–2019) in this assessment, of which 0.02 W m <sup>–2</sup> is due to the 7 ppb increase in concentrations, and 0.02 W m <sup>–2</sup> to the tropospheric adjustment. As the adjustments are assessed to be small there remains ''high confidence'' in the overall assessment.

Increased nitrous oxide leads to ozone depletion in the upper stratosphere which will make a positive contribution to the direct ERF here (Section 6.4.2 and Figure 6.12) when considering emissions-based estimates of ERF.

<div id="7.3.2.4" class="h3-container"></div>

<span id="halogenated-species"></span>
==== 7.3.2.4 Halogenated Species ====

<div id="h3-7-siblings" class="h3-siblings"></div>

The stratospheric-temperature-adjusted radiative efficiencies (SARF per ppb increase in concentration) for halogenated compounds are reviewed extensively in [[#Hodnebrog--2020a|Hodnebrog et al. (2020a)]] , an update to those used in AR5. Many halogenated compounds have lifetimes short enough that they can be considered short-lived climate forcers (SLCFs; Table 6.1). As such, they are not completely ‘well-mixed’ and their vertical distributions are taken into account when determining their radiative efficiencies. The World Meteorological Organization ( [[#WMO--2018|WMO, 2018]] ) updated the lifetimes of many halogenated compounds and these were used in [[#Hodnebrog--2020a|Hodnebrog et al. (2020a)]] .

The tropospheric adjustments to chlorofluorocarbons (CFCs), specifically CFC-11 and CFC-12, have been quantified as 13% ± 10% and 12% ± 14% of the SARF, respectively ( [[#Hodnebrog--2020b|Hodnebrog et al., 2020b]] ). The assessed adjustment to CFCs is therefore 12% ± 13% with ''low confidence'' due to the lack of corroborating studies. There have been no calculations for other halogenated species so for these the tropospheric adjustments are therefore assumed to be 0 ± 13% with ''low confidence.'' The radiative modelling uncertainties are 14% and 24% for compounds with lifetimes greater than and less than five years, respectively ( [[#Hodnebrog--2020a|Hodnebrog et al., 2020a]] ). The overall uncertainty in the ERFs of halogenated compounds is therefore assessed to be 19% and 26% depending on the lifetime. The ERF from CFCs is slowly decreasing, but this is compensated for by the increased forcing from the replacement species (HCFCs and HFCs). The ERF from HFCs has increased by 0.028 ± 0.05 W m <sup>–2</sup> . Thus, the concentration changes mean that the total ERF from halogenated compounds has increased since AR5 from 0.360 ± 0.036 W m <sup>–2</sup> to 0.408 ± 0.078 W m <sup>–2</sup> (Table 7.5). Of this, 0.034 W m <sup>–2</sup> is due to increased radiative efficiencies and tropospheric adjustments, and 0.014 W m <sup>–2</sup> is due to increases in concentrations. As the adjustments are assessed to be small there remains ''high confidence'' in the overall assessment.

Halogenated compounds containing chlorine and bromine lead to ozone depletion in the stratosphere which will reduce the associated ERF ( [[#Morgenstern--2020|Morgenstern et al., 2020]] ). [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Section 6.4 and Figure 6.12) assesses the ERF contributions due to the chemical effects of reactive gases.

<div id="7.3.2.5" class="h3-container"></div>

<span id="ozone"></span>
==== 7.3.2.5 Ozone ====

<div id="h3-8-siblings" class="h3-siblings"></div>

Estimates of the pre-industrial to present-day tropospheric ozone radiative forcing are based entirely on models. The lack of pre-industrial ozone measurements prevents an observational determination. There have been limited studies of ozone ERFs ( [[#MacIntosh--2016|MacIntosh et al., 2016]] ; [[#Xie--2016|Xie et al., 2016]] ; [[#Skeie--2020|Skeie et al., 2020]] ). [[#Skeie--2020|Skeie et al. (2020)]] found little net contribution to the ERF from tropospheric adjustment terms for 1850–2000 change in ozone (tropospheric and stratospheric ozone combined), although [[#MacIntosh--2016|MacIntosh et al. (2016)]] suggested that increases in stratospheric or upper tropospheric ozone reduces high-cloud and increases low-cloud, whereas an increase in lower tropospheric ozone reduces low-cloud. Further studies suggest that changes in circulation due to decreases in stratospheric ozone affect Southern Hemisphere clouds and the atmospheric levels of sea salt aerosol that would contribute additional adjustments, possibly of comparable magnitude to the SARF from stratospheric ozone depletion ( [[#Grise--2013|Grise et al., 2013]] , 2014; [[#Xia--2016|Xia et al., 2016]] , 2020). ESM responses to changes in ozone-depleting substances (ODS) in CMIP6 show a much more negative ERF than would be expected from offline calculations of SARF ( [[#Morgenstern--2020|Morgenstern et al., 2020]] ; [[#Thornhill--2021b|Thornhill et al., 2021b]] ) again suggesting a negative contribution from adjustments. However there is insufficient evidence available to quantify this effect.

Without sufficient information to assess whether the ERFs differ from SARF, this assessment relies on offline radiative transfer calculations of SARF for both tropospheric and stratospheric ozone. [[#Checa-Garcia--2018|Checa-Garcia et al. (2018)]] found SARF of 0.30 W m <sup>–2</sup> for changes in ozone (1850–1860 to 2009–2014). These were based on precursor emissions and ODS concentrations from the Coupled Chemistry Model Initiative (CCMI) project ( [[#Morgenstern--2017|Morgenstern et al., 2017]] ). [[#Skeie--2020|Skeie et al. (2020)]] calculated an ozone SARF of 0.41 ± 0.12 W m <sup>–2</sup> (1850–2010; from five climate models and one chemistry transport model) using CMIP6 precursor emissions and ODS concentrations (excluding models without fully interactive ozone chemistry and one model with excessive ozone depletion). The ozone precursor emissions are higher in CMIP6 than in CCMI, which explains much of the increase compared to [[#Checa-Garcia--2018|Checa-Garcia et al. (2018)]] ''.''

Previous assessments have split the ozone forcing into tropospheric and stratospheric components. This does not correspond to the division between ozone production and ozone depletion and is sensitive to the choice of tropopause ( ''high confidence'' ) ( [[#Myhre--2013b|Myhre et al., 2013b]] ). The contributions to total SARF in CMIP6 ( [[#Skeie--2020|Skeie et al., 2020]] ) are 0.39 ± 0.07 and 0.02 ± 0.07 W m <sup>–2</sup> for troposphere and stratosphere respectively (using a 150 ppb ozone tropopause definition). This small positive (but with uncertainty encompassing negative values) stratospheric ozone SARF is due to contributions from ozone precursors to lower stratospheric ozone and some of the CMIP6 models showing ozone depletion in the upper stratosphere, where depletion contributes a positive radiative forcing ( ''medium confidence'' ).

As there is insufficient evidence to quantify adjustments, for total ozone the assessed central estimate for ERF is assumed to be equal to SARF ( ''low confidence'' ) and follows [[#Skeie--2020|Skeie et al. (2020)]] , since that study uses the most recent emissions data. The dataset is extended over the entire historical period following [[#Skeie--2020|Skeie et al. (2020)]] , with a SARF for 1750–1850 of 0.03 W m <sup>–2</sup> and for 2010–2018 of 0.03 W m <sup>–2</sup> , <sup></sup> to give 0.47 [0.24 to 0.70] W m <sup>–2</sup> for 1750–2019. This maintains the 50% uncertainty (5–95% range) from AR5 which is largely due to the uncertainty in pre-industrial emissions ( [[#Rowlinson--2020|Rowlinson et al., 2020]] ). There is also ''high confidence'' that this range includes uncertainty due to the adjustments. The CMIP6 SARF is more positive than the AR5 value of 0.31 W m <sup>–2</sup> for the period 1850–2011 ( [[#Myhre--2013b|Myhre et al., 2013b]] ) which was based on the Atmospheric Chemistry and Climate Intercomparison Project (ACCMIP; [[#Shindell--2013|Shindell et al., 2013]] ) ''.'' The assessment is sensitive to the assumptions on precursor emissions used to drive the models, which are larger in CMIP6 than ACCMIP.

In summary, although there is insufficient evidence to quantify adjustments, there is ''high confidence'' in the assessed range of ERF for ozone changes over the 1750–2019 period, giving an assessed ERF of 0.47 [0.24 to 0.70] W m <sup>–2</sup> .

<div id="7.3.2.6" class="h3-container"></div>

<span id="stratospheric-water-vapour"></span>
==== 7.3.2.6 Stratospheric Water Vapour ====

<div id="h3-9-siblings" class="h3-siblings"></div>

This section considers direct anthropogenic effects on stratospheric water vapour by oxidation of methane. Since AR5 the SARF from methane-induced stratospheric water vapour changes has been calculated in [[#Winterstein--2019|Winterstein et al., 2019]] , corresponding to 0.09 W m <sup>–2</sup> when scaling to 1850 to 2014 methane changes. This is marginally larger than the AR5 assessed value of 0.07 ± 0.05 W m <sup>–2</sup> ( [[#Myhre--2013b|Myhre et al., 2013b]] ). [[#Wang--2020|Wang and Huang (2020)]] quantified the adjustment terms to a stratospheric water vapour change equivalent to a forcing from a 2×CO <sub>2</sub> warming (which has a different vertical profile). They found that the ERF was less than 50% of the SARF due to high-cloud decrease and upper tropospheric warming. The assessed ERF is therefore 0.05 ± 0.05 W m <sup>–2</sup> with a lower limit reduced to zero and the central value and upper limit reduced to allow for adjustment terms. This still encompasses the two recent SARF studies. There is ''medium confidence'' in the SARF from agreement with the recent studies and AR5. There is ''low confidence'' in the adjustment terms.

Stratospheric water vapour may also change as an adjustment to species that warm or cool the upper troposphere–lower stratosphere region ( [[#Forster--2005|Forster and Joshi, 2005]] ; [[#Stuber--2005|Stuber et al., 2005]] ), in which case it should be included as part of the ERF for that compound. Changes in GSAT are also associated with changes in stratospheric water vapour as part of the water-vapour–climate feedback ( [[#7.4.2.2|Section 7.4.2.2]] ).

<div id="7.3.2.7" class="h3-container"></div>

<span id="synthesis"></span>
==== 7.3.2.7 Synthesis ====

<div id="h3-10-siblings" class="h3-siblings"></div>

The ERF of GHGs (excluding ozone and stratospheric water vapour) over 1750–2019 is assessed to be 3.32 ± 0.29 W m <sup>–2</sup> . It has increased by 0.49 W m <sup>–2</sup> compared to AR5 (reference year 2011) ( ''high confidence'' ) ''.'' Most of this has been due to an increase in CO <sub>2</sub> concentration since 2011 [0.27 ± 0.03] W m <sup>–2</sup> , with concentration increases in CH <sub>4</sub> , N <sub>2</sub> O and halogenated compounds adding 0.02, 0.02 and 0.01 W m <sup>–2</sup> respectively (Table 7.5). Changes in the radiative efficiencies (including adjustments) of CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O and halogenated compounds have increased the ERF by an additional 0.15 W m <sup>–2</sup> compared to the AR5 values ( ''high confidence'' ). Note that the ERFs in this section do not include chemical effects of GHGs on production or destruction of ozone or aerosol formation (Section 6.2.2). The ERF for ozone is considerably increased compared to AR5 due to an increase in the assumed ozone precursor emissions in CMIP6 compared to CMIP5, and better accounting for the effects of both ozone precursors and ODSs in the stratosphere. The ERF for stratospheric water vapour is slightly reduced. The combined ERF from ozone and stratospheric water vapour has increased since AR5 by 0.10 ± 0.50 W m <sup>–2</sup> ( ''high confidence'' ), although the uncertainty ranges still include the AR5 values.

<div id="_idContainer025" class="Basic-Text-Frame"></div>

'''Table 7.5''' '''|''' '''Present-day mole fractions in parts per trillion (pmol mol''' –1 '''), except where specified, and effective radiative forcing (ERF, in W m''' –2 ''') for the well-mixed greenhouse gases (WMGHGs).''' Data taken from ( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] [[IPCC:Wg1:Chapter:Chapter-2#2.2.3|Section 2.2.3]] ). The data for 2011 (the time of the AR5 estimates) are also shown. Some of the concentrations vary slightly from those reported in AR5 owing to averaging different data sources. Individual species are reported where 1750–2019 ERF is at least 0.001 W m <sup>–2</sup> . Radiative efficiencies for the minor gases are given in Supplementary Material, Table 7.SM.7. Uncertainties in the ERF for all gases are dominated by the uncertainties in the radiative efficiencies. Tabulated global mixing ratios of all WMGHGs and ERFs from 1750 to 2019 are provided in Annex III.

{| class="wikitable"
|-
|
| colspan="4"| Concentration

| colspan="2"| ERF with Respect to 1850

| colspan="2"| ERF with Respect to 1750

|-
|
| 2019

| 2011

| 1850

| 1750

| 2019

| 2011

| 2019

| 2011

|-
| CO <sub>2</sub> (ppm)

| 409.9

| 390.5

| 285.5

| 278.3

| 2.012 ± 0.241

| 1.738

| 2.156 ± 0.259

| 1.882

|-
| CH <sub>4</sub> (ppb)

| 1866.3

| 1803.3

| 807.6

| 729.2

| 0.496 ± 0.099

| 0.473

| 0.544 ± 0.109

| 0.521

|-
| N <sub>2</sub> O (ppb)

| 332.1

| 324.4

| 272.1

| 270.1

| 0.201 ± 0.030

| 0.177

| 0.208 ± 0.031

| 0.184

|-
| HFC-134a

| 107.6

| 62.7

| 0.0

| 0.0

| 0.018

| 0.010

| 0.018

| 0.010

|-
| HFC-23

| 32.4

| 24.1

| 0.0

| 0.0

| 0.006

| 0.005

| 0.006

| 0.005

|-
| HFC-32

| 20.0

| 4.7

| 0.0

| 0.0

| 0.002

| 0.001

| 0.002

| 0.001

|-
| HFC-125

| 29.4

| 10.3

| 0.0

| 0.0

| 0.007

| 0.002

| 0.007

| 0.002

|-
| HFC-143a

| 24.0

| 12.0

| 0.0

| 0.0

| 0.004

| 0.002

| 0.004

| 0.002

|-
| SF <sub>6</sub>

| 10.0

| 7.3

| 0.0

| 0.0

| 0.006

| 0.004

| 0.006

| 0.004

|-
| CF <sub>4</sub>

| 85.5

| 79.0

| 34.0

| 34.0

| 0.005

| 0.004

| 0.005

| 0.004

|-
| C <sub>2</sub> f <sub>6</sub>

| 4.8

| 4.2

| 0.0

| 0.0

| 0.001

| 0.001

| 0.001

| 0.001

|-
| CFC-11

| 226.2

| 237.3

| 0.0

| 0.0

| 0.066

| 0.070

| 0.066

| 0.070

|-
| CFC-12

| 503.1

| 528.6

| 0.0

| 0.0

| 0.180

| 0.189

| 0.180

| 0.189

|-
| CFC-113

| 69.8

| 74.6

| 0.0

| 0.0

| 0.021

| 0.022

| 0.021

| 0.022

|-
| CFC-114

| 16.0

| 16.3

| 0.0

| 0.0

| 0.005

| 0.005

| 0.005

| 0.005

|-
| CFC-115

| 8.7

| 8.4

| 0.0

| 0.0

| 0.002

| 0.002

| 0.002

| 0.002

|-
| HCFC-22

| 246.8

| 213.2

| 0.0

| 0.0

| 0.053

| 0.046

| 0.053

| 0.046

|-
| HCFC-141b

| 24.4

| 21.4

| 0.0

| 0.0

| 0.004

| 0.003

| 0.004

| 0.003

|-
| HCFC-142b

| 22.3

| 21.2

| 0.0

| 0.0

| 0.004

| 0.004

| 0.004

| 0.004

|-
| CCl <sub>4</sub>

| 77.9

| 86.1

| 0.0

| 0.0

| 0.013

| 0.014

| 0.013

| 0.014

|-
| Sum of HFCs (HFC-134a equivalent)

| 237.1

| 128.6

| 0.0

| 0.0

| 0.040

| 0.022

| 0.040

| 0.022

|-
| Sum of CFCs+HCFCs+other ozone depleting gases covered by the Montreal Protocol (CFC-12 equivalent)

| 1031.9

| 1050.1

| 0.0

| 0.0

| 0.354

| 0.362

| 0.354

| 0.362

|-
| Sum of PFCs (CF <sub>4</sub> equivalent)

| 109.4

| 98.9

| 34.0

| 34.0

| 0.007

| 0.006

| 0.007

| 0.006

|-
| Sum of Halogenated species

|
| 0.408 ±0.078

| 0.394

| 0.408 ±0.078

| 0.394

|-
| Total

|
| 3.118 ±0.258

| 2.782

| 3.317 ±0.278

| 2.981

|}

<div id="7.3.3" class="h2-container"></div>

<span id="aerosols"></span>
=== 7.3.3 Aerosols ===

<div id="h2-11-siblings" class="h2-siblings"></div>

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).

<div id="7.3.3.1" class="h3-container"></div>

<span id="aerosolradiation-interactions"></span>
==== 7.3.3.1 Aerosol–Radiation Interactions ====

<div id="h3-11-siblings" class="h3-siblings"></div>

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.

<div id="7.3.3.1.1" class="h4-container"></div>

<span id="observation-based-lines-of-evidence"></span>
===== 7.3.3.1.1 Observation-based lines of evidence =====

<div id="h4-1-siblings" class="h4-siblings"></div>

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 ''.''

<div id="7.3.3.1.2" class="h4-container"></div>

<span id="model-based-lines-of-evidence"></span>
===== 7.3.3.1.2 Model-based lines of evidence =====

<div id="h4-2-siblings" class="h4-siblings"></div>

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> .

<div id="7.3.3.1.3" class="h4-container"></div>

<span id="overall-assessment-of-irfari-and-erfari"></span>

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

<div id="h4-3-siblings" class="h4-siblings"></div>

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.

<div id="_idContainer026" class="Basic-Text-Frame"></div>

'''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

|}

<div id="7.3.3.2" class="h3-container"></div>

<span id="aerosolcloud-interactions"></span>
==== 7.3.3.2 Aerosol–Cloud Interactions ====

<div id="h3-12-siblings" class="h3-siblings"></div>

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.

<div id="7.3.3.2.1" class="h4-container"></div>

<span id="observation-based-evidence"></span>
===== 7.3.3.2.1 Observation-based evidence =====

<div id="h4-4-siblings" class="h4-siblings"></div>

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> .

<div id="_idContainer027" class="Basic-Text-Frame"></div>

'''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>
=== 7.3.4 Other Agents ===

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In addition to the large anthropogenic ERFs associated with WMGHGs and atmospheric aerosols assessed in Sections 7.3.2 and 7.3.3, land-use change, contrails and aviation-induced cirrus, and light-absorbing particles deposited on snow and ice have also contributed to the overall anthropogenic ERF and are assessed in Sections 7.3.4.1, 7.3.4.2 and 7.3.4.3. Changes in solar irradiance, galactic cosmic rays, and volcanic eruptions since pre-industrial times combined represent the natural contribution to the total (anthropogenic + natural) ERF and are discussed in Sections 7.3.4.4, 7.3.4.5 and 7.3.4.6.

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==== 7.3.4.1 Land Use ====

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Land-use forcing is defined as those changes in land-surface properties directly caused by human activity rather than by climate processes (see also [[IPCC:Wg1:Chapter:Chapter-2#2.2.7|Section 2.2.7]] ). Land-use change affects the surface albedo. For example, deforestation typically replaces darker forested areas with brighter cropland, and thus imposes a negative radiative forcing on climate, while afforestation and reforestation can have the opposite effect. Precise changes depend on the nature of the forest, crops and underlying soil. Land-use change also affects the amount of water transpired by vegetation ( [[#Devaraju--2015|Devaraju et al., 2015]] ). Irrigation of land directly affects evaporation ( [[#Sherwood--2018|Sherwood et al., 2018]] ), causing a global increase of 32,500 m <sup>3</sup> s <sup>−1</sup> due to human activity. Changes in evaporation and transpiration affect the latent heat budget, but do not directly affect the top-of-atmosphere (TOA) radiative fluxes. The lifetime of water vapour is so short that the effect of changes in evaporation on the greenhouse contribution of water vapour are negligible ( [[#Sherwood--2018|Sherwood et al., 2018]] ). However, evaporation can affect the ERF through adjustments, particularly through changes in low-cloud amounts. Land management affects the emissions or removal of GHGs from the atmosphere (such as CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O). These emissions changes have the greatest effect on climate ( [[#Ward--2014|Ward et al., 2014]] ), however they are already included in GHG inventories. Land-use change also affects the emissions of dust and biogenic volatile organic compounds (BVOCs), which form aerosols and affect the atmospheric concentrations of ozone and methane (Section 6.2.2). The effects of land use on surface temperature and hydrology were recently assessed in SRCCL ( [[#Jia--2019|Jia et al., 2019]] ).

Using the definition of ERF from ( [[#7.1|Section 7.1]] , the adjustment in land-surface temperature is excluded from the definition of ERF, but changes in vegetation and snow cover (resulting from land-use change) are included ( [[#Boisier--2013|Boisier et al., 2013]] ). Land-use change in the mid-latitudes induces a substantial amplifying adjustment in snow cover. Few climate model studies have attempted to quantify the ERF of land-use change. T. [[#Andrews--2017|]] [[#Andrews--2017|Andrews et al. (2017)]] calculated a very large surface albedo ERF (–0.47 W m <sup>–2</sup> ) from 1860 to 2005 in the HadGEM2-ES model, although they did not separate out the surface albedo change from snow cover change. HadGEM2-ES is known to overestimate the amount of boreal trees and shrubs in the unperturbed state ( [[#Collins--2011|Collins et al., 2011]] ) so will tend to overestimate the ERF associated with land-use change. The increases in dust in HadGEM2-ES contributed an extra –0.25 W m <sup>–2</sup> , whereas cloud cover changes added a small positive adjustment (0.15 W m <sup>–2</sup> ) consistent with a reduction in transpiration. A multi-model quantification of land-use forcing in CMIP6 models (excluding one outlier) ( [[#Smith--2020b|Smith et al., 2020b]] ) found an IRF of –0.15 ± 0.12 W m <sup>–2</sup> (1850–2014), and an ERF (correcting for land-surface temperature change) of –0.11 ± 0.09 W m <sup>–2</sup> . This shows a small positive adjustment term (mainly from a reduction in cloud cover). CMIP5 models show an IRF of –0.11 [–0.16 to –0.04] W m <sup>–2</sup> (1850–2000) after excluding unrealistic models ( [[#Lejeune--2020|Lejeune et al., 2020]] ).

The contribution of land-use change to albedo changes has recently been investigated using MODIS and AVHRR to attribute surface albedo to geographically specific land-cover types ( [[#Ghimire--2014|Ghimire et al., 2014]] ). When combined with a historical land-use map ( [[#Hurtt--2011|Hurtt et al., 2011]] ) this gives a SARF of –0.15 ± 0.01 W m <sup>–2</sup> for the period 1700–2005, of which approximately –0.12 W m <sup>–2</sup> is from 1850. This study accounted for correlations between vegetation type and snow cover, but not the adjustment in snow cover identified in T. [[#Andrews--2017|]] [[#Andrews--2017|Andrews et al. (2017)]] .

The indirect contributions of land-use change through biogenic emissions is very uncertain. Decreases in BVOCs reduce ozone and methane ( [[#Unger--2014|Unger, 2014]] ), but also reduce the formation of organic aerosols and their effects on clouds ( [[#Scott--2017|Scott et al., 2017]] ). Adjustments through changes in aerosols and chemistry are model dependent ( [[#Zhu--2019b|Zhu et al., 2019b]] ; [[#Zhu--2020|Zhu and Penner, 2020]] ), and it is not yet possible to make an assessment based on a limited number of studies.

The contribution of irrigation (mainly to low-cloud amount) is assessed as –0.05 <sup></sup> [–0.1 to 0.05] W m <sup>–2</sup> for the historical period ( [[#Sherwood--2018|Sherwood et al., 2018]] ).

Because the CMIP5 and CMIP6 modelling studies are in agreement with [[#Ghimire--2014|Ghimire et al. (2014)]] , that study is used as the assessed albedo ERF. Adding the irrigation effect to this gives an overall assessment of the ERF from land-use change of –0.20 ± 0.10 W m <sup>–2</sup> ( ''medium confidence'' ). Changes in ERF since 2014 are assumed to be small compared to the uncertainty, so this ERF applies to the period 1750–2019. The uncertainty range includes uncertainties in the adjustments.

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==== 7.3.4.2 Contrails and Aviation-induced Cirrus ====

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ERF from contrails and aviation-induced cirrus is taken from the assessment of [[#Lee--2020|Lee et al. (2020)]] , at 0.057 [0.019 to 0.098] W m <sup>–2</sup> in 2018 (see Section 6.6.2 for an assessment of the total effects of aviation). This is rounded up to address its ''low confidence'' and the extra year of air traffic to give an assessed ERF over 1750–2019 of 0.06 [0.02 to 0.10] W m <sup>–2</sup> . This assessment is given ''low confidence'' due to the potential that processes missing from the assessment would affect the magnitude of contrails and aviation-induced cirrus ERF.

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==== 7.3.4.3 Light-absorbing Particles on Snow and Ice ====

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In AR5, it was assessed that the effects of light-absorbing particles (LAPs) did probably not significantly contribute to recent reductions in Arctic ice and snow ( [[#Vaughan--2013|Vaughan et al., 2013]] ). The SARF from LAPs on snow and ice was assessed to 0.04 [0.02 to 0.09] W m <sup>–2</sup> ( [[#Boucher--2013|Boucher et al., 2013]] ), a range appreciably lower than the estimates given in AR4 ( [[#Forster--2007|Forster et al., 2007]] ). This effect was assessed to be ''low confidence'' ( ''medium evidence'' , ''low agreement'' ) (Table 8.5 in [[#Myhre--2013b|Myhre et al., 2013b]] ).

Since AR5 there has been progress in the understanding of the physical state and processes in snow that govern the albedo reduction by black carbon (BC). The SROCC ( [[#IPCC--2019a|IPCC, 2019a]] ) assessed that there is ''high confidence'' that darkening of snow by deposition of BC and other light-absorbing aerosol species increases the rate of snow melt ( [[IPCC:Wg1:Chapter:Chapter-2#2.2|Section 2.2]] in [[#Hock--2019|Hock et al., 2019]] ; [[IPCC:Wg1:Chapter:Chapter-3#3.4|Section 3.4]] in [[#Meredith--2019|Meredith et al., 2019]] ). C. [[#He--2018|]] [[#He--2018|He et al. (2018)]] found that taking into account both the non-spherical shape of snow grains and internal mixing of BC in snow significantly altered the effects of BC on snow albedo. The reductions of snow albedo by dust and BC have been measured and characterized in the Arctic, the Tibetan Plateau, and mid-latitude regions subject to seasonal snowfall, including North America and northern and eastern Asia ( [[#Qian--2015|Qian et al., 2015]] ).

Since AR5, two further studies of global IRF from black carbon on snow deposition are available, with best estimates of 0.01 W m <sup>–2</sup> ( [[#Lin--2014|Lin et al., 2014]] ) and 0.045 W m <sup>–2</sup> ( [[#Namazi--2015|Namazi et al., 2015]] ). Organic carbon deposition on snow and icehas been estimated to contribute a small positive IRF of 0.001 to 0.003 W m <sup>–2</sup> ( [[#Lin--2014|Lin et al., 2014]] ). No comprehensive global assessments of mineral dust deposition on snow are available, although the effects are potentially large in relation to the total effect of LAPs on snow and ice forcing ( [[#Yasunari--2015|Yasunari et al., 2015]] ).

Most radiative forcing estimates have a regional emphasis. The regional focus makes estimating a global mean radiative forcing from aggregating different studies challenging, and the relative importance of each region is expected to change if the global pattern of emissions sources changes ( [[#Bauer--2013|Bauer et al., 2013]] ). The lower bound of the assessed range of BC on snow and ice is extended to zero to encompass [[#Lin--2014|Lin et al. (2014)]] , with the best estimate unchanged, resulting in 0.04 [0.00 to 0.09] W m <sup>–2</sup> . The efficacy of BC on snow forcing was estimated to be 2 to 4 times as large as for an equivalent CO <sub>2</sub> forcing as the effects are concentrated at high latitudes in the cryosphere ( [[#Bond--2013|Bond et al., 2013]] ). However, it is unclear how much of this effect is due to radiative adjustments leading to a higher ERF, and how much comes from a less negative feedback α due to the high-latitude nature of the forcing. To estimate the overall ERF, the IRF is doubled assuming that part of the increased efficacy is due to adjustments. This gives an overall assessed ERF of +0.08 [0.00 to 0.18] W m <sup>–2</sup> , with ''low confidence'' .

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==== 7.3.4.4 Solar ====

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Variations in the total solar irradiance (TSI) represent a natural external forcing agent. The dominant cycle is the solar 11-year activity cycle, which is superimposed on longer cycles ( [[IPCC:Wg1:Chapter:Chapter-2#2.2|Section 2.2]] ). Over the last three 11-year cycles, the peak-to-trough amplitude in TSI has differed by about 1 W m <sup>–2</sup> between solar maxima and minima (Figure 2.2).

The fractional variability in the solar irradiance, over the solar cycle and between solar cycles, is much greater at short wavelengths in the 200–400 nanometre (nm) band than for the broad visible/infrared band that dominates TSI ( [[#Krivova--2006|Krivova et al., 2006]] ). The IRF can be derived simply by Δ ''TSI'' × (1 – albedo)/4 irrespective of wavelength, where the best estimate of the planetary albedo is usually taken to be 0.29 and Δ ''TSI'' represents the change in total solar irradiance ( [[#Stephens--2015|Stephens et al., 2015]] ). (The factor 4 arises because TSI is per unit area of Earth cross section presented to the Sun and IRF is per unit area of Earth’s surface). The adjustments are expected to be wavelength dependent. [[#Gray--2009|Gray et al. (2009)]] determined a stratospheric temperature adjustment of –22% to spectrally resolved changes in the solar radiance over one solar cycle. This negative adjustment is due to stratospheric heating from increased absorption by ozone at the short wavelengths, increasing the outgoing longwave radiation to space. A multi-model comparison ( [[#Smith--2018b|Smith et al., 2018b]] ) calculated adjustments of –4% due to stratospheric temperatures and –6% due to tropospheric processes (mostly clouds), for a change in TSI across the spectrum (Figure 7.4). The smaller magnitude of the stratospheric temperature adjustment is consistent with the broad spectral change rather than the shorter wavelengths characteristic of solar variation. A single-model study also found an adjustment that acts to reduce the forcing ( [[#Modak--2016|Modak et al., 2016]] ). While there has not yet been a calculation based on the appropriate spectral change, the –6% tropospheric adjustment from [[#Smith--2018b|Smith et al. (2018b)]] is adopted along with the [[#Gray--2009|Gray et al. (2009)]] stratospheric temperature adjustment. The ERF due to solar variability over the historical period is therefore represented by 0.72 × Δ ''TSI'' × (1 – albedo)/4 using the TSI timeseries from ( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] ).

The AR5 ( [[#Myhre--2013b|Myhre et al., 2013b]] ) assessed solar SARF from around 1750 to 2011 to be 0.05 [0.00 to 0.10] W m <sup>–2</sup> which was computed from the seven-year mean around the solar minima in 1745 (being closest to 1750) and 2008 (being the most recent solar minimum). The inclusion of tropospheric adjustments that reduce ERF (compared to SARF in AR5) has a negligible effect on the overall forcing. Prior to the satellite era, proxy records are used to reconstruct historical solar activity. In AR5, historical records were constructed using observations of solar magnetic features. In this assessment historical time series are constructed from radiogenic compounds in the biosphere and in ice cores that are formed from cosmic rays ( [[#Steinhilber--2012|Steinhilber et al., 2012]] ).

In this assessment the TSI from the Paleoclimate Model Intercomparison Project Phase 4 (PMIP4) reconstruction is used ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] ; [[#Jungclaus--2017|Jungclaus et al., 2017]] ). Proxies constructed from the <sup>14</sup> C and <sup>10</sup> Be radiogenic records for the SATIRE-M model ( [[#Vieira--2011|Vieira et al., 2011]] ) and <sup>14</sup> C record for the PMOD model ( [[#Shapiro--2011|Shapiro et al., 2011]] ) for the 1745 solar minimum provide ERFs for 1745–2008 of –0.01, –0.02 and 0.00 W m <sup>–2</sup> respectively. An independent dataset from the National Oceanic and Atmospheric Administration’s Climate Data Record ( [[#Coddington--2016|Coddington et al., 2016]] ; [[#Lean--2018|Lean, 2018]] ) provides an ERF for 1745–2008 of +0.03 W m <sup>–2</sup> . One substantially higher ERF estimate of +0.35 W m <sup>–2</sup> derived from TSI reconstructions is provided by [[#Egorova--2018|Egorova et al. (2018)]] . However, the estimate from [[#Egorova--2018|Egorova et al. (2018)]] hinges on assumptions about long-term changes in the quiet Sun for which there is no observed evidence. [[#Lockwood--2020|Lockwood and Ball (2020)]] analysed the relationship between observed changes in cosmic ray fluxes and recent, more accurate, TSI data and derived ERF between –0.01 and +0.02 W m <sup>–2</sup> , and [[#Yeo--2020|Yeo et al. (2020)]] modelling showed the maximum possible ERF to be 0.26 ± 0.09 W m <sup>–2</sup> . Hence the [[#Egorova--2018|Egorova et al. (2018)]] estimate is not explicitly taken into account in the assessment presented in this section.

In contrast to AR5, the solar ERF in this assessment uses full solar cycles rather than solar minima. The pre-industrial TSI is defined as the mean from all complete solar cycles from the start of the <sup>14</sup> C SATIRE-M proxy record in 6755 BCE to 1744 CE. The mean TSI from solar cycle 24 (2009–2019) is adopted as the assessment period for 2019. The best estimate solar ERF is assessed to be 0.01 W m <sup>–2</sup> , using the <sup>14</sup> C reconstruction from SATIRE-M, with a ''likely'' range of –0.06 to +0.08 W m <sup>–2</sup> ( ''medium confidence'' ). The uncertainty range is adopted from the evaluation of [[#Lockwood--2020|Lockwood and Ball (2020)]] using a Monte Carlo analysis of solar activity from the Maunder Minimum to 2019 from several datasets, leading to an ERF of –0.12 to +0.15 W m <sup>–2</sup> . The [[#Lockwood--2020|Lockwood and Ball (2020)]] full uncertainty range is halved as the period of reduced solar activity in the Maunder Minimum had ended by 1750 ( ''medium confidence'' ).

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==== 7.3.4.5 Galactic Cosmic Rays ====

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Variations in the flux of galactic cosmic rays (GCR) reaching the atmosphere are modulated by solar activity and affect new particle formation in the atmosphere through their link to ionization of the troposphere ( [[#Lee--2019|Lee et al., 2019]] ). It has been suggested that periods of high GCR flux correlate with increased aerosol and CCN concentrations and therefore also with cloud properties (e.g., [[#Dickinson--1975|Dickinson, 1975]] ; [[#Kirkby--2007|Kirkby, 2007]] ).

Since AR5, the link between GCR and new particle formation has been more thoroughly studied, particularly by experiments in the CERN CLOUD chamber (Cosmics Leaving OUtdoor Droplets; [[#Dunne--2016|Dunne et al., 2016]] ; [[#Kirkby--2016|Kirkby et al., 2016]] ; [[#Pierce--2017|Pierce, 2017]] ). By linking the GCR-induced new particle formation from CLOUD experiments to CCN, [[#Gordon--2017|Gordon et al. (2017)]] found that the CCN concentration for low-clouds differed by 0.2–0.3% between solar maximum and solar minimum. Combined with relatively small variations in the atmospheric ion concentration over centennial time scales ( [[#Usoskin--2015|Usoskin et al., 2015]] ), it is therefore unlikely that cosmic ray intensity affects present-day climate via nucleation ( [[#Yu--2014|Yu and Luo, 2014]] ; [[#Dunne--2016|Dunne et al., 2016]] ; [[#Pierce--2017|Pierce, 2017]] ; [[#Lee--2019|Lee et al., 2019]] ).

Studies continue to seek a relationship between GCR and properties of the climate system based on correlations and theory. [[#Svensmark--2017|Svensmark et al. (2017)]] proposed a new mechanism for ion-induced increase in aerosol growth rate and subsequent influence on the CCN concentration. The study does not include an estimate of the resulting effect on atmospheric CCN concentration and cloud radiative properties. Furthermore, Svensmark et al. (2009, 2016) find correlations between GCRs and aerosol and cloud properties in satellite and ground-based data. Multiple studies investigating this link have challenged such correlations ( [[#Kristjánsson--2008|Kristjánsson et al., 2008]] ; [[#Calogovic--2010|Calogovic et al., 2010]] ; [[#Laken--2016|Laken, 2016]] ).

AR5 concluded that the GCR effect on CCN is too weak to have any detectable effect on climate and no robust association was found between GCR and cloudiness ( [[#Boucher--2013|Boucher et al., 2013]] ). Published literature since AR5 robustly supports these conclusions with key laboratory, theoretical and observational evidence. There is ''high confidence'' that GCRs contribute a negligible ERF over the period 1750–2019.

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==== 7.3.4.6 Volcanic Aerosols ====

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There is large episodic negative radiative forcing associated with sulphur dioxide (SO <sub>2</sub> ) being ejected into the stratosphere from explosive volcanic eruptions, accompanied by more frequent smaller eruptions (Figure 2.2 and Cross-Chapter Box 4.1). From SO <sub>2</sub> gas, reflective sulphate aerosol is formed in the stratosphere where it may persist for months to years, reducing the incoming solar radiation. The volcanic SARF in AR5 ( [[#Myhre--2013b|Myhre et al., 2013b]] ) was derived by scaling the stratospheric aerosol optical depth (SAOD) by a factor of –25 W m <sup>–2</sup> per unit SAOD from [[#Hansen--2005b|Hansen et al. (2005b)]] . Quantification of the adjustments to SAOD perturbations from climate model simulations have determined a significant positive adjustment driven by a reduction in cloud amount (Figure 7.4; [[#Marshall--2020|Marshall et al., 2020]] ). Analysis of CMIP5 models provides a mean ERF of –20 W m <sup>–2</sup> per unit SAOD ( [[#Larson--2016|Larson and Portmann, 2016]] ). Single-model studies with successive generations of Hadley Centre climate models produce estimates between –17 and –19 W m <sup>–2</sup> per unit SAOD ( [[#Gregory--2016|Gregory et al., 2016]] ; [[#Marshall--2020|Marshall et al., 2020]] ), with some evidence that ERF may be non-linear with SAOD for large eruptions ( [[#Marshall--2020|Marshall et al., 2020]] ). Analysis of the volcanically active periods of 1982–1985 and 1990–1994 using the CESM1(WACCM) aerosol–climate model provided an SAOD-to-ERF relationship of –21.5 (± 1.1) W m <sup>–2</sup> per unit SAOD ( [[#Schmidt--2018|Schmidt et al., 2018]] ). Volcanic SO <sub>2</sub> emissions may contribute a positive forcing through effects on upper tropospheric ice clouds, due to additional ice nucleation on volcanic sulphate particles ( [[#Friberg--2015|Friberg et al., 2015]] ; [[#Schmidt--2018|Schmidt et al., 2018]] ), although one observational study found no significant effect ( [[#Meyer--2015|Meyer et al., 2015]] ). Due to ''low agreement'' , the contribution of sulphate aerosol effects on ice clouds to volcanic ERF is not included in the overall assessment.

Non-explosive volcanic eruptions generally yield negligible global ERFs due to the short atmospheric lifetimes (a few weeks) of volcanic aerosols in the troposphere. However, as discussed in ( [[#7.3.3.2|Section 7.3.3.2]] , the massive fissure eruption in Holuhraun, Iceland persisted for months in 2014 and 2015 and did in fact result in a marked and persistent reduction in cloud droplet radii and a corresponding increase in cloud albedo regionally ( [[#Malavelle--2017|Malavelle et al., 2017]] ). This shows that non-explosive fissure eruptions can lead to strong regional and even global ERFs, but because the Holuhraun eruption occurred in Northern Hemisphere winter, solar insolation was weak and the observed albedo changes therefore did not result in an appreciable global ERF ( [[#Gettelman--2015|Gettelman et al., 2015]] ).

The ERF for volcanic stratospheric aerosols is assessed to be –20 ± 5 W m <sup>–2</sup> per unit SAOD ( ''medium confidence'' ) based on the CMIP5 multi-model mean from the [[#Larson--2016|Larson and Portmann (2016)]] SAOD forcing efficiency calculations combined with the single-model results of [[#Gregory--2016|Gregory et al. (2016)]] , [[#Schmidt--2018|Schmidt et al. (2018)]] and [[#Marshall--2020|Marshall et al. (2020)]] . This is applied to the SAOD time series from ( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] [[IPCC:Wg1:Chapter:Chapter-2#2.2.2|Section 2.2.2]] ) to generate a time series of ERF and temperature response shown in ( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] (Figure 2.2 and Figure 7.8, respectively). The period from 500 BCE to 1749 CE, spanning back to the start of the record of [[#Toohey--2017|Toohey and Sigl (2017)]] , is defined as the pre-industrial baseline and the volcanic ERF is calculated using an SAOD anomaly from this long-term mean. As in AR5, a pre-industrial to present-day ERF assessment is not provided due to the episodic nature of volcanic eruptions.

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<span id="synthesis-of-global-mean-radiative-forcing-past-and-future"></span>
=== 7.3.5 Synthesis of Global Mean Radiative Forcing, Past and Future ===

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==== 7.3.5.1 Major Changes in Forcing since the IPCC Fifth Assessment Report ====

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The AR5 introduced the concept of effective radiative forcing (ERF) and radiative adjustments, and made a preliminary assessment that the tropospheric adjustments were zero for all species other than the effects of aerosol–cloud interaction and black carbon. Since AR5, new studies have allowed for a tentative assessment of values for tropospheric adjustments to CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O, some CFCs, solar forcing, and stratospheric aerosols, and to place a tighter constraint on adjustments from aerosol–cloud interaction (Sections 7.3.2, 7.3.3 and 7.3.4). In AR6, the definition of ERF explicitly removes the land-surface temperature change as part of the forcing, in contrast to AR5 where only sea surface temperatures were fixed. The ERF is assessed to be a better predictor of modelled equilibrium temperature change (i.e., less variation in feedback parameter) than SARf ( [[#7.3.1|Section 7.3.1]] ).

As discussed in ( [[#7.3.2|Section 7.3.2]] , the radiative efficiencies for CO <sub>2</sub> , CH <sub>4</sub> and N <sub>2</sub> O have been updated since AR5 ( [[#Etminan--2016|Etminan et al., 2016]] ). There has been a small (1%) increase in the stratospheric-temperature-adjusted CO <sub>2</sub> radiative efficiency, and a +5% tropospheric adjustment has been added. The stratospheric-temperature-adjusted radiative efficiency for CH <sub>4</sub> is increased by approximately 25% ( ''high confidence'' ). The tropospheric adjustment is tentatively assessed to be –14% ( ''low confidence'' ). A +7% tropospheric adjustment has been added to the radiative efficiency for N <sub>2</sub> O and +12% to CFC-11 and CFC-12 ( ''low confidence'' ).

For aerosols there has been a convergence of model and observational estimates of aerosol forcing, and the partitioning of the total aerosol ERF has changed. Compared to AR5 a greater fraction of the ERF is assessed to come from ERFaci compared to the ERFari. It is now assessed as ''virtually certain'' that the total aerosol ERF (ERFari+aci) is negative.

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<span id="summary-erf-assessment"></span>
==== 7.3.5.2 Summary ERF Assessment ====

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Figure 7.6 shows the industrial-era ERF estimates for 1750 to 2019 for the concentration change in different forcing agents. The assessed uncertainty distributions for each individual component are combined with a 100,000-member Monte Carlo simulation that samples the different distributions, assuming they are independent, to obtain the overall assessment of total present-day ERF (Supplementary Material 7.SM.1). The corresponding emissions-based ERF figure is shown in ( [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Figure 6.12).

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[[File:5b77ae447f35f1ef8b2afd934611d5d8 IPCC_AR6_WGI_Figure_7_6.png]]

'''Figure 7.6''' '''|''' '''Change in effective radiative forcing (ERF) from 1750 to 2019 by contributing forcing agents (carbon dioxide, other well-mixed greenhouse gases (WMGHGs), ozone, stratospheric water vapour, surface albedo, contrails and aviation-induced cirrus, aerosols, anthropogenic total, and solar).''' Solid bars represent best estimates, and ''very likely'' (5–95%) ranges are given by error bars. Non-CO <sub>2</sub> WMGHGs are further broken down into contributions from methane (CH <sub>4</sub> ), nitrous oxide (N <sub>2</sub> O) and halogenated compounds. Surface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. Aerosols are broken down into contributions from aerosol–cloud interactions (ERFaci) and aerosol–radiation interactions (ERFari). For aerosols and solar, the 2019 single-year values are given (Table 7.8), which differ from the headline assessments in both cases. Volcanic forcing is not shown due to the episodic nature of volcanic eruptions. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

The total anthropogenic ERF over the industrial era (1750–2019) is estimated as 2.72 [1.96 to 3.48] W m <sup>–2</sup> ( ''high confidence'' ) (Table 7.8 and Annex III) ''.'' This represents a 0.43 W m <sup>–2</sup> increase over the assessment made in AR5 ( [[#Myhre--2013b|Myhre et al., 2013b]] ) for the period 1750–2011. This increase is a result of compensating effects. Atmospheric concentration increases of GHGs since 2011 and upwards revisions of their forcing estimates have led to a 0.59 W m <sup>–2</sup> increase in their ERF. However, the total aerosol ERF is assessed to be more negative compared to AR5, due to revised estimates rather than trends ( ''high confidence'' ) ''.''

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'''Table 7.8''' '''|''' '''Summary table of effective radiative forcing (ERF) estimates for AR6 and comparison with the four previous IPCC assessment reports.''' Prior to AR5 values are stratospheric-temperature-adjusted radiative forcing (SARF). For AR5 aerosol–radiation interactions (ari) and aerosol–cloud interactions (aci) are ERF; all other values assume ERF equals SARF. Ranges shown are 5–95%. Volcanic ERF is not added to the table due to the episodic nature of volcanic eruptions which makes it difficult to compare to the other forcing mechanisms. Solar ERF is based on total solar irradiance (TSI) and not spectral variation.

{| class="wikitable"
|-
| rowspan="2"| '''Driver'''

| colspan="6"| '''Global Mean Effective Radiative Forcing (W m''' <sup>–2</sup> ''')'''

|-
| SAR

(1750–1993)

| TAR

(1750–1998)

| AR4

(1750–2005)

| AR5

(1750–2011)

| AR6

(1750–2019)

| Comment

|-
| CO <sub>2</sub>

| 1.56 [1.33 to 1.79]

| 1.46 [1.31 to 1.61]

| 1.66 [1.49 to 1.83]

| 1.82 [1.63 to 2.01]

| 2.16 [1.90 to 2.41]

| rowspan="4"| Increases in concentrations. Changes to radiative efficiencies.

Inclusion of tropospheric adjustments.

|-
| CH <sub>4</sub>

| 0.47 [0.40 to 0.54

| 0.48 [0.41 to 0.55]

| 0.48 [0.43 to 0.53]

| 0.48 [0.43 to 0.53]

| 0.54 [0.43 to 0.65]

|-
| N <sub>2</sub> O

| 0.14 [0.12 to 0.16]

| 0.15 [0.14 to 0.16]

| 0.16 [0.14 to 0.18]

| 0.17 [0.14 to 0.20]

| 0.21 [0.18 to 0.24]

|-
| Halogenated species

| 0.26 [0.22 to 0.30]

| 0.36 [0.31 to 0.41]

| 0.33 [0.30 to 0.36]

| 0.36 [0.32 to 0.40]

| 0.41 [0.33 to 0.49]

|-
| Tropospheric ozone

| 0.4 [0.2 to 0.6]

| 0.35 [0.20 to 0.50]

| 0.35 [0.25 to 0.65]

| 0.40 [0.20 to 0.60]

| rowspan="2"| 0.47 [0.24 to 0.71]

| rowspan="2"| Revised precursor emissions. No tropospheric adjustment assessed. No troposphere–stratosphere separation.

|-
| Stratospheric ozone

| –0.1 [–0.2 to –0.05]

| –0.15 [–0.25 to –0.05]

| –0.05 [–0.15 to 0.05]

| –0.05 [–0.15 to 0.05]

|-
| Stratospheric water vapour

| Not estimated

| [0.01 to 0.03]

| 0.07 [0.02 to 0.1]

| 0.07 [0.02 to 0.12]

| 0.05 [0.00 to 0.10]

| Downward revision due to adjustments.

|-
| Aerosol–radiation interactions

| –0.5 [–0.25 to –1.0]

| Not estimated

| –0.50 [–0.90 to –0.10]

| –0.45 [–0.95 to 0.05]

| –0.22 [–0.47 to 0.04]

| ERFari magnitude reduced by about 50% compared to AR5, based on agreement between observation-based and modelling-based evidence.

|-
| Aerosol–cloud interactions

| [–1.5 to 0.0]

(sulphate only)

| [–2.0 to 0.0]

(all aerosols)

| –0.7 [–1.8 to –0.3]

(all aerosols)

| –0.45 [–1.2 to 0.0]

| –0.84 [–1.45 to –0.25]

| ERFaci magnitude increased by about 85% compared to AR5, based on agreement between observation-based and modelling-based lines of evidence.

|-
| Land use

| Not estimated

| –0.2 [–0.4 to 0.0]

| –0.2 [–0.4 to 0.0]

| –0.15 [–0.25 to –0.05]

| –0.20 [–0.30 to –0.10]

| Includes irrigation.

|-
| Surface albedo (black + organic carbon aerosol on snow and ice)

| Not estimated

| Not estimated

| 0.10 [0.00 to 0.20]

| 0.04 [0.02 to 0.09]

| 0.08 [0.00 to 0.18]

| Increased since AR5 to better account for temperature effects.

|-
| Combined contrails and aviation-induced cirrus

| Not estimated

| [0.00 to 0.04]

| Not estimated

| 0.05 [0.02 to 0.15]

| 0.06 [0.02 to 0.10]

| Narrower range since AR5.

|-
| Total anthropogenic

| Not estimated

| Not estimated

| 1.6 [0.6 to 2.4]

| 2.3 [1.1 to 3.3]

| '''2.72 [1.96 to 3.48]'''

| Increase due to GHGs, compensated slightly by aerosol ERFaci.

|-
| Solar irradiance

| 0.3 [0.1 to 0.5]

| 0.3 [0.1 to 0.5]

| 0.12 [0.06 to 0.30]

| 0.05 [0.0 to 0.10]

| 0.01 [–0.06 to 0.08]

| Revised historical TSI estimates and methodology.

|}

Greenhouse gases, including ozone and stratospheric water vapour from methane oxidation, are estimated to contribute an ERF of 3.84 [3.46 to 4.22] W m <sup>–2</sup> over 1750–2019. Carbon dioxide continues to contribute the largest part (56 ± 16%) of this GHG ERF ( ''high confidence'' ).

As discussed in ( [[#7.3.3|Section 7.3.3]] , aerosols have in total contributed an ERF of –1.1 [–1.7 to –0.4] W m <sup>–2</sup> over 1750–2019 ( ''medium confidence'' ). Aerosol–cloud interactions contribute approximately 75–80% of this ERF with the remainder due to aerosol–radiation interactions (Table 7.8).

For the purpose of comparing forcing changes with historical temperature change ( [[#7.5.2|Section 7.5.2]] ), longer averaging periods are useful. The change in ERF from the second half of the 19th century (1850–1900) compared with a recent period (2006–2019) is +2.20 [1.53 to 2.91] W m <sup>–2</sup> , of which 1.71 [1.51 to 1.92] W m <sup>–2</sup> is due to CO <sub>2</sub> .

<div id="7.3.5.3" class="h3-container"></div>

<span id="temperature-contribution-of-forcing-agents"></span>
==== 7.3.5.3 Temperature Contribution of Forcing Agents ====

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The estimated contribution of forcing agents to the 2019 global surface air temperature (GSAT) change relative to 1750 is shown in Figure 7.7. These estimates were produced using the concentration-derived ERF time series presented in Figure 2.10 and described in Supplementary Material 7.SM.1.3. The resulting GSAT changes over time are shown in Figure 7.8. The historical time series of ERFs for the WMGHGs can be derived by applying the ERF calculations of [[#7.3.2|Section 7.3.2]] to the observed time series of WMGHG concentrations in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] [[IPCC:Wg1:Chapter:Chapter-2#2.2|Section 2.2]] .

<div id="_idContainer034" class="Basic-Text-Frame"></div>

[[File:8816a7abd93d5e458b5595d9e373f9ef IPCC_AR6_WGI_Figure_7_7.png]]

'''Figure 7.7''' '''|''' '''The contribution of forcing agents to 2019 temperature change relative to 1750 produced using the two-layer emulator (Supplementary Material 7.SM.2), constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1.''' The results are from a 2237-member ensemble. Temperature contributions are expressed for carbon dioxide, other well-mixed greenhouse gases (WMGHGs), ozone, stratospheric water vapour, surface albedo, contrails and aviation-induced cirrus, aerosols, solar, volcanic, and total. Solid bars represent best estimates, and ''very likely'' (5–95%) ranges are given by error bars. Dashed error bars show the contribution of forcing uncertainty alone, using best estimates of ECS (3.0°C), TCR (1.8°C) and two-layer model parameters representing the CMIP6 multi-model mean. Solid error bars show the combined effects of forcing and climate response uncertainty using the distribution of ECS and TCR from Tables 7.13 and 7.14, and the distribution of calibrated model parameters from 44 CMIP6 models. Non-CO <sub>2</sub> WMGHGs are further broken down into contributions from methane (CH <sub>4</sub> ), nitrous oxide (N <sub>2</sub> O) and halogenated compounds. Surface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. Aerosols are broken down into contributions from aerosol–cloud interactions (ERFaci) and aerosol–radiation interactions (ERFari). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

These ERF timeseries are combined with a two-layer emulator (Cross-Chapter Box 7.1 and Supplementary Material 7.SM.2) using a 2237-member constrained Monte Carlo sample of both forcing uncertainty (by sampling ERF ranges) and climate response (by sampling ECS, TCR and ocean heat capacity ranges). The net model warming over the historical period is matched to the assessment of historical GSAT warming from 1850–1900 to 1995–2014 of 0.85 [0.67 to 0.98] °C (Cross-Chapter Box 2.3) and ocean heat content change from 1971 to 2018 [[#7.2.2.2|Section 7.2.2.2]] ). Therefore the model gives the breakdown of the GSAT trend associated with different forcing mechanisms that are consistent with the overall GSAT change. The model assumes that there is no variation in feedback parameter across forcing mechanisms ( [[#7.3.1|Section 7.3.1]] ) and variations in the effective feedback parameter over the historical record ( [[#7.4.4|Section 7.4.4]] ). The distribution of ECS was informed by [[#7.5.5|Section 7.5.5]] and chosen to approximately maintain the best estimate and ''likely'' / ''very likely'' ranges assessed in that section (see also Supplementary Material 7.SM.2). The TCR has an ensemble median value of 1.81°C, in good agreement with ( [[#7.5.5|Section 7.5.5]] . Two error bars are shown in Figure 7.7. The dashed error bar shows the contribution of ERF uncertainty (as assessed in the subsections of ( [[#7.3|Section 7.3]] ) employing the best estimate of climate response with an ECS of 3.0°C. The solid bar is the total response uncertainty using the ( [[#7.5.5|Section 7.5.5]] assessment of ECS. The uncertainty in the historical temperature contributions ofthe different forcing agents is mostly due to uncertainties in ERF, yet for the WMGHG the uncertainty is dominated by the climate response as its ERF is relatively well known (Figure 7.7). From the assessment of emulator responses in Cross-Chapter Box 7.1, there is ''high confidence'' that calibrated emulators such as the one employed here can represent the historical GSAT change between 1850–1900 and 1995–2014 to within 5% for the best estimate and 10% for the ''very likely'' range (Supplementary Material, Table 7.SM.4). This gives ''high confidence'' in the overall assessment of GSAT change for the response to ERFs over 1750–2019 derived from the emulator.

The total human forced GSAT change from 1750 to 2019 is calculated to be 1.29 [1.00 to 1.65] °C ( ''high confidence'' ). Although the total emulated GSAT change has ''high confidence'' , the confidence of the individual contributions matches those given for the ERF assessment in the subsections of ( [[#7.3|Section 7.3]] . The calculated GSAT change is comprised of a WMGHG warming of 1.58 [1.17 to 2.17] °C ( ''high confidence'' ) '','' a warming from ozone changes of 0.23 [0.11 to 0.39] °C ( ''high confidence'' ), and a cooling of –0.50 [–0.22 to –0.96] °C from aerosol effects ( ''medium confidence'' ). The aerosol cooling has considerable regional time dependence (Section 6.4.3) but has weakened slightly over the last 20 years in the global mean (Figures 2.10 and 7.8). There is also a –0.06 [–0.15 to +0.01] °C contribution from surface reflectance changes which is dominated by land-use change ( ''medium confidence'' ). Changes in solar and volcanic activity are assessed to have together contributed a small change of –0.02 [–0.06 to +0.02] °C since 1750 ( ''medium confidence'' ).

The total (anthropogenic + natural) emulated GSAT between 1850–1900 and 2010–2019 is 1.14 [0.89 to 1.45] °C, compared to the assessed GSAT of 1.06 [0.88 to 1.21] °c ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] and Cross Chapter Box 2.3). The emulated response is slightly warmer than the observations and has a larger uncertainty range. As the emulated response attempts to constrain to multiple lines of evidence (Supplementary Material 7.SM.2), only one of which is GSAT, they should not necessarily be expected to exactly agree. The larger uncertainty range in the emulated GSAT compared to the observations is reflective of the uncertainties in ECS, TCR and ERF (particularly the aerosol ERF) that drive the emulator response.

The emulator gives a range of GSAT response for the period 1750 to 1850–1900 of 0.09 [0.04 to 0.14] °C from anthropogenic ERFs. These results are used as a line of evidence for the assessment of this change in ( [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] (Cross-Chapter Box 1.2), which gives an overall assessment of 0.1°C [ ''likely'' range –0.1 to +0.3] °C.

Figure 7.8 presents the GSAT time series using ERF time series for individual forcing agents rather than their aggregation. It shows that for most of the historical period the long time scale total GSAT trend estimate from the emulator closely follows the CO <sub>2</sub> contribution. The GSAT estimate from non-CO <sub>2</sub> greenhouse gas forcing (from other WMGHGs and ozone) has been approximately cancelled out in the global average by a cooling GSAT trend from aerosols. However, since 1980 the aerosol cooling trend has stabilized and may have started to reverse, so that over the last few decades the long-term warming has been occurring at a faster rate than would be expected due to CO <sub>2</sub> alone ( ''high confidence'' ) (see also Sections 2.2.6 and 2.2.8). Throughout the record, but especially prior to 1930, periods of volcanic cooling dominate decadal variability. These estimates of the forced response are compared with model simulations and attributable warming estimates in ( [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] [[IPCC:Wg1:Chapter:Chapter-3#3.3.1|Section 3.3.1]] ).

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[[File:0945047867fb2c01188bdd985141e85b IPCC_AR6_WGI_Figure_7_8.png]]

'''Figure 7.8''' '''|''' '''Attributed global surface air temperature change (GSAT) from 1750 to 2019 produced using the two-layer emulator (Supplementary Material 7.SM.2), forced with ERF derived in this chapter (displayed in Figure 2.10) and climate response constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1.''' The results shown are the medians from a 2237-member ensemble that encompasses uncertainty in forcing and climate response (year-2019 best estimates and uncertainties are shown in Figure 7.7 for several components). Temperature contributions are expressed for carbon dioxide (CO <sub>2</sub> ), methane (CH <sub>4</sub> ), nitrous oxide (N <sub>2</sub> O), other well-mixed greenhouse gases (WMGHGs), ozone (O <sub>3</sub> ), aerosols, and other anthropogenic forcings, as well as total anthropogenic, solar, volcanic, and total forcing. Shaded uncertainty bands show ''very likely'' (5–95%) ranges. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

<div id="cross-chapter-box-7.1" class="h2-container box-container"></div>

'''Cross-Chapter Box 7.1 | Physical Emulation of Earth System Models for Scenario Classification and Knowledge Integration in AR6'''

<div id="h2-14-siblings" class="h2-siblings"></div>

'''Contributors:''' Zebedee R.J. Nicholls (Australia), Malte Meinshausen (Australia/Germany), Piers Forster (United Kingdom), Kyle Armour (United States of America), Terje Berntsen (Norway), William Collins (United Kingdom), Christopher Jones (United Kingdom), Jared Lewis (Australia/New Zealand), Jochem Marotzke (Germany), Sebastian Milinski (Germany), Joeri Rogelj (United Kingdom/Belgium), Chris Smith (United Kingdom)

Climate model emulators are simple physically based models that are used to approximate large-scale climate responses of complex Earth system models (ESMs). Due to their low computational cost they can populate or span wide uncertainty ranges that ESMs cannot. They need to be calibrated to do this and, once calibrated, they can aid inter-ESM comparisons and act as ESM extrapolation tools to reflect and combine knowledge from ESMs and many other lines of evidence ( [[#Geoffroy--2013a|Geoffroy et al., 2013a]] ; [[#Good--2013|Good et al., 2013]] ; [[#Smith--2018a|Smith et al., 2018a]] ). In AR6, the term ‘climate model emulator’ (or simply ‘emulator’) is preferred over ‘simple’ or ‘reduced-complexity climate model’ to reinforce their use as specifically calibrated tools (Cross-Chapter Box 7.1, Figure 1). Nonetheless, simple physically based climate models have a long history of use in previous IPCC reports ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.3.4|Section 1.5.3.4]] ). Climate model emulators can include carbon and other gas cycles and can combine uncertainties along the cause–effect chain, from emissions to temperature response. AR5 (M. [[#Collins--2013|]] [[#Collins--2013|Collins et al., 2013]] ) used the MAGICC6 emulator ( [[#Meinshausen--2011a|Meinshausen et al., 2011a]] ) in a probabilistic setup ( [[#Meinshausen--2009|Meinshausen et al., 2009]] ) to explore the uncertainty in future projections. A simple impulse response emulator ( [[#Good--2011|Good et al., 2011]] ) was also used to ensure a consistent set of ESM projections could be shown across a range of scenarios. [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] in AR5 WGI ( [[#Myhre--2013b|Myhre et al., 2013b]] ) employed a two-layer emulator for quantifying global temperature-change potentials (GTP). In AR5 WGIII ( [[#Clarke--2014|Clarke et al., 2014]] ), MAGICC6 was also used for the classification of scenarios, and in AR5 Synthesis Report ( [[#IPCC--2014|IPCC, 2014]] ) this information was used to estimate carbon budgets. In SR1.5, two emulators were used to provide temperature projections of scenarios: the MAGICC6 model, which was used for the scenario classification, and the FaIR1.3 model ( [[#Millar--2017|Millar et al., 2017]] ; [[#Smith--2018a|Smith et al., 2018a]] ).

<div id="_idContainer038" class="Body-copy_Boxes_Blue-Boxes_•-Box-body"></div>

[[File:845ed83893e4876bd52e87882f372c58 IPCC_AR6_WGI_CCBox_7_1_Figure_1.png]]

'''Cross-Chapter Box 7.1, Figure''' '''1 |''' '''A comparison between the global surface air temperature (GSAT) response of various calibrated simple climate models, assessed ranges and Earth system models (ESMs). (a)''' and '''(b)''' compare the assessed historical GSAT time series ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] ) with four multi-gas emulators calibrated to replicate numerous assessed ranges (panel (a); Cross-Chapter Box 7.1, Table 2) and also compares idealized CO <sub>2</sub> -only concentration scenario response for one ESM (IPSL CM6A-LR) and multiple emulators which participated in RCMIP Phase 1 ( [[#Nicholls--2020|Nicholls et al., 2020]] ) calibrated to that single ESM (panel (b)). '''(c)''' and '''(d)''' compare this Report’s assessed ranges for GSAT warming (Box 4.1) under the multi-gas scenario SSP1-2.6 with the same calibrated emulators as in (a). For context, a range of CMIP6 ESM results are also shown (thin lines in (c) and open circles in (d)). Panel (b) adapted from [[#Nicholls--2020|Nicholls et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

The SR1.5 found that the physically based emulators produced different projected non-CO <sub>2</sub> forcing and identified the largely unexplained differences between the two emulators used as a key knowledge gap ( [[#Forster--2018|Forster et al., 2018]] ). This led to a renewed effort to test the skill of various emulators. The Reduced Complexity Model Intercomparison Project (RCMIP; [[#Nicholls--2020|Nicholls et al., 2020]] ) found that the latest generation of the emulators can reproduce key characteristics of the observed changes in global surface air temperature (GSAT) together with other key responses of ESMs (Cross-Chapter Box 7.1, Figure 1a). In particular, despite their reduced structural complexity, some emulators are able to replicate the non-linear aspects of ESM GSAT response over a range of scenarios. GSAT emulation has been more thoroughly explored in the literature than other types of emulation. Structural differences between emulation approaches lead to different outcomes and there are problems with emulating particular ESMs. In conclusion, there is ''medium confidence'' that emulators calibrated to single ESM runs can reproduce ESM projections of the forced GSAT response to other similar emissions scenarios to within natural variability ( [[#Meinshausen--2011b|Meinshausen et al., 2011b]] ; [[#Geoffroy--2013a|Geoffroy et al., 2013a]] ; [[#Dorheim--2020|Dorheim et al., 2020]] ; [[#Nicholls--2020|Nicholls et al., 2020]] ; [[#Tsutsui--2020|Tsutsui, 2020]] ), although larger differences can remain for scenarios with very different forcing characteristics. For variables other than GSAT there has not yet been a comprehensive effort to evaluate the performance of emulators.

'''Application of emulators in AR6 WGI'''
Cross-Chapter Box 7.1 Table 1 shows the use of emulators within the WGI Report. The main use of emulation in the Report is to estimate GSAT change from effective radiative forcing (ERF) or concentration changes, where various versions of a two-layer energy budget emulator are used. The two-layer emulator is equivalent to a two-timescale impulse-response model (Supplementary Material 7.SM.2; [[#Geoffroy--2013b|Geoffroy et al., 2013b]] ). Both a single configuration version and probabilistic forms are used. The emulator is an extension of the energy budget equation (Box 7.1, Equation 7.1) and allows for heat exchange between the upper- and deep-ocean layers, mimicking the ocean heat uptake that reduces the rate of surface warming under radiative forcing ( [[#Gregory--2000|Gregory, 2000]] ; [[#Held--2010|Held et al., 2010]] ; [[#Winton--2010|Winton et al., 2010]] ; [[#Armour--2017|Armour, 2017]] ; [[#Mauritsen--2017|Mauritsen and Pincus, 2017]] ; [[#Rohrschneider--2019|Rohrschneider et al., 2019]] ). Although the same energy budget emulator approach is used, different calibrations are employed in various sections, to serve different purposes and keep lines of evidence as independent as possible. [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] additionally employs projections of ocean heat content from the ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] two-layer emulator to estimate the thermostatic component of future sea level rise ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3|Section 9.6.3]] and Supplementary Material 7.SM.2).

'''Cross-Chapter Box 7.1, Table 1'''

'''|'''

'''Use of emulation within the WGI Report.'''
{| class="wikitable"
|-
| '''Section'''

| '''Application and Emulator Type'''

| '''Emulated Variables'''

|-
| Cross Chapter-Box 1.2

| Estimate anthropogenic temperature change pre-1850, based on radiative forcing time series from Chapter 7. Uses the ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] calibrated two-layer emulator: a two-layer energy budget emulator, probabilistically calibrated to AR6 ECS, TCR, historical warming and ocean heat uptake ranges, driven by the ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] concentration-based ERFs.

| GSAT

|-
| [[IPCC:Wg1:Chapter:Chapter-3#3.3|Section 3.3]]

[[#7.3|Section 7.3]]

| Investigation of the historical temperature response to individual forcing mechanisms to complement detection and attribution results. Uses the ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] calibrated two-layer emulator.

| GSAT

|-
| Box 4.1

| Understanding the spread in GSAT increase of CMIP6 models and comparison to other assessments; assessment of contributions to projected temperature uncertainty. Uses a two-layer emulator calibrated to the ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] ECS and TCR assessment driven by ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] best-estimate ERFs.

| GSAT

|-
| [[IPCC:Wg1:Chapter:Chapter-4#4.6|Section 4.6]]

| Emulators used to assess differences in radiative forcing and GSAT response between RCP and SSP scenarios. Uses the ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] ERF time series and the MAGICC7 probabilistic emissions-driven emulator for GSAT calibrated to the WGI assessment.

| ERF, GSAT

|-
| [[IPCC:Wg1:Chapter:Chapter-4#4.7|Section 4.7]]

| Emulator used for long-term GSAT projections (post-2100) to complement the small number of ESMs with data beyond 2100. Uses the MAGICC7 probabilistic emissions-driven emulator calibrated to the WGI assessment.

| GSAT

|-
| [[IPCC:Wg1:Chapter:Chapter-5#5.5|Section 5.5]]

| Estimated non-CO <sub>2</sub> warming contributions of mitigation scenarios at the time of their net zero CO <sub>2</sub> emissions for integration in the assessment of remaining carbon budgets. Uses the MAGICC7 probabilistic emissions-driven emulator calibrated to the WGI assessment.

| GSAT

|-
| Section 6.6

Section 6.7

| Estimated contributions to future warming from SLCFs across SSP scenarios based on ERF time series. Uses a single two-layer emulator configuration derived from the medians of MAGICC7 and FaIRv1.6.2 AR6 WG1 GSAT probabilistic responses and the best-estimate of ECS and TCR.

| GSAT

|-
| [[#7.5|Section 7.5]]

| Estimating a process-based TCR from a process-based ECS. Uses a two-layer emulator in probabilistic form calibrated to process-based estimates from Chapter 7; a different calibration compared to the main ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] emulator.

| TCR

|-
| [[#7.6|Section 7.6]]

| Deriving emissions metrics. Uses two-layer emulator configurations derived from MAGICC7 and FaIRv1.6.2 AR6 WG1 probabilistic GSAT responses.

| GTPs and their uncertainties

|-
| [[IPCC:Wg1:Chapter:Chapter-9#9.6|Section 9.6]]

| Deriving global mean sea level projections. Uses the ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] calibrated two-layer emulator for GSAT and ocean heat content, where GSAT drives regional statistical emulators of ice sheets and glaciers.

| Sea level and ice loss

|-
| [[IPCC:Wg1:Chapter:Chapter-11#11.2|Section 11.2]] and Cross-Chapter Box 11.1

| Regional patterns of response are compared to global mean trends. Assessed literature includes projections with a regional pattern scaling and variability emulator.

| Various regional information

|}

Emissions-driven emulators (as opposed to ERF-driven or concentration-driven emulators) are also used in the Report. In ( [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] [[IPCC:Wg1:Chapter:Chapter-4#4.6|Section 4.6]] ) MAGICC7 is used to emulate GSAT beyond 2100 since its long-term response has been assessed to be fit-for-purpose to represent the behaviour of ESMs. In ( [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] [[IPCC:Wg1:Chapter:Chapter-5#5.5|Section 5.5]] ) MAGICC7 is used to explore the non-CO <sub>2</sub> GSAT contribution in emissions scenarios. In ( [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] and ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7 Chapter 7] [[#7.6|Section 7.6]] ), two-layer model configurations are tuned to match the probabilistic GSAT responses of FaIRv1.6.2 and MAGICC7 emissions-driven emulators. For ( [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] the two median values from FaIRv1.6.2 and MAGICC7 emulators are averaged and then matched to the best-estimate ECS of 3°C and TCR of 1.8°C (Tables 7.13 and 7.14) under the best-estimate ERF due to a doubling of CO <sub>2</sub> of 3.93 W m <sup>–2</sup> (Table 7.4). For ( [[#7.6|Section 7.6]] a distribution of responses is used from the two emulators to estimate uncertainties in global temperature change potentials (GTP).

'''Emissions-driven emulators for scenario classification in AR6 WGIII'''
As in AR5 and SR1.5, emissions-driven emulators are used to communicate outcomes of the physical climate science assessment and uncertainties to quantify the temperature outcome associated with different emissions scenarios. In particular, the computational efficiency of these emulators allows the analysis of a large number of multi-gas emissions scenarios in terms of multiple characteristics, e.g., year of peak temperature or 2030 emissions levels, in line with keeping global warming to below 1.5°C or 2.0°C.

Four emissions-driven emulators have been considered as tools for WGIII to explore the range of GSAT response to multiple scenarios beyond those assessed in WGI. The four emulators are CICERO-SCM ( [[#Skeie--2017|Skeie et al., 2017]] , 2021), FaIRv1.6.2 ( [[#Millar--2017|Millar et al., 2017]] ; [[#Smith--2018a|Smith et al., 2018a]] ), MAGICC7 ( [[#Meinshausen--2009|Meinshausen et al., 2009]] ) and OSCARv3.1.1 ( [[#Gasser--2017a|Gasser et al., 2017a]] , 2020). Each emulator’s probabilistic distribution has been calibrated to capture the relationship between emissions and GSAT change. The calibration is informed by the WGI assessed ranges of ECS, TCR, historical GSAT change, ERF, carbon cycle metrics and future warming projections under the (concentration-driven) SSP scenarios. The emulators are then provided as a tool for WGIII to perform a GSAT-based classification of mitigation scenarios consistent with the physical understanding assessed in WGI. The calibration step reduced the emulator differences identified in SR1.5. Note that evaluation of both central and range estimates of each emulator’s probabilistic projections is important to assess the fitness-for-purpose for the classification of scenarios in WGIII, based on information beyond the central estimate of GSAT warming.

MAGICC7 and FaIRv1.6.2 emissions-based emulators are able to represent the WGI assessment to within small differences (defined here as within typical rounding precisions of ±5% for central estimates and ±10% for ranges) across more than 80% of metric ranges (Cross-Chapter Box 7.1, Table 2). Both calibrated emulators are consistent with assessed ranges of ECS, historical GSAT, historical ocean heat uptake, total greenhouse gas ERF, methane ERF and the majority of the assessed SSP warming ranges. FaIRv1.6.2 also matches the assessed central value of TCRE and airborne fraction. Whereas, MAGICC7 matches the assessed TCR ranges as well as providing a closer fit to the SSP warming ranges for the lower-emissions scenarios. In the evaluation framework considered here, CICERO-SCM represents historical warming to within 2% of the assessed ranges and also represents future temperature ranges across the majority of the assessment, although it lacks the representation of the carbon cycle. In this framework, OSCARv3.1.1 is less able to represent the assessed projected GSAT ranges although it matches the range of airborne fraction estimates closely and the assessed historical GSAT ''likely'' range to within 0.5%. Despite these identified limitations, both CICERO-SCM and OSCARv3.1.1 provide additional information for evaluating the sensitivity of scenario classification to model choice.

How emulators match the assessed ranges used for the evaluation framework is summarized here and in Table 2. The first is too-low projections for 2081–2100 under SSP1-1.9 (8% or 15% too low for the central estimate and 15% or 25% too low for the lower end in the case of MAGICC7 or FaIRv1.6.2, respectively). The second is the representation of the aerosol ERF (both MAGICC7 and FaIRv1.6.2 are greater than 8% less negative than the central assessed range and greater than 10% less negative for the lower assessed range), as energy balance models struggle to reproduce an aerosol ERF with a magnitude as strong as the assessed best estimate and still match historical warming estimates. Both emulators have medium to large differences compared to the TCRE and airborne fraction ranges (see notes beneath Cross-Chapter Box 7.1, Table 2). Finally, there is also a slight overestimate of the low end of the assessed historical GSAT range.

Overall, there is ''high confidence'' that emulated historical and future ranges of GSAT change can be calibrated to be internally consistent with the assessment of key physical-climate indicators in this Report: greenhouse gas ERFs, ECS and TCR. When calibrated to match the assessed ranges of GSAT and multiple physical climate indicators, physically based emulators can reproduce the best estimate of GSAT change over 1850–1900 to 1995–2014 to within 5% and the ''very likely'' range of this GSAT change to within 10%. MAGICC7 and FaIRv1.6.2 match at least two-thirds of the ( [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] assessed projected GSAT changes to within these levels of precision.

'''Cross-C'''

'''hapter Box 7.1, Table 2 |'''

'''Percentage differences between the emulator value and the WGI assessed best estimate and range for key metrics.''' Values are given for four emulators in their respective AR6-calibrated probabilistic setups. Absolute values of these indicators are shown in Supplementary Material, Table 7.SM.4.
[[File:db097ac9538c30576c63850f2c1749cb IPCC_AR6_WGI_Chapter_7_CCB_7_1_Table_2_1.jpg]] [[File:c2a1a7fc511714ff89cec55e5f9aec8f IPCC_AR6_WGI_Chapter_7_CCB_7_1_Table_2_2.jpg]]
{| class="wikitable"
|-
| colspan="2"| '''Emulator'''

| colspan="3"| '''CICERO-SCM'''

| colspan="3"| '''FaIRv1.6.2'''

| colspan="3"| '''MAGICC7'''

| colspan="3"| '''OSCARv3.1.1'''

|-
| colspan="2"| '''Assessed Range'''

| '''Lower'''

| '''Central'''

| '''Upper'''

| '''Lower'''

| '''Central'''

| '''Upper'''

| '''Lower'''

| '''Central'''

| '''Upper'''

| '''Lower'''

| '''Central'''

| '''Upper'''

|-
| colspan="14"| '''Key metrics'''

|-
| colspan="2"| '''ECS (°C)'''

| 26%

| 2%

| –18%

| 3%

| –2%

| 1%

| –3%

| –1%

| –3%

| –8%

| –15%

| –22%

|-
| colspan="2"| '''TCRE (°C per 1000 GtC)**'''

|
| 29%

| –7%

| –21%

| 37%

| 5%

| –5%

| 50%

| –8%

| –20%

|-
| colspan="2"| '''TCR (°C)'''

| 15%

| –5%

| –3%

| 14%

| 0%

| 3%

| 6%

| 4%

| 9%

| 26%

| 1%

| –14%

|-
| colspan="14"| '''Historical warming and Effective Radiative Forcing'''

|-
| colspan="2"| '''GSAT warming (°C)'''

1995–2014 rel. 1850–1900

| 2%

| 0%

| 0%

| 7%

| 3%

| 4%

| 7%

| 1%

| –1%

| –0%

| –8%

| –0%

|-
| colspan="2"| '''Ocean heat content change (ZJ)*'''

1971–2018

| –24%

| –27%

| –29%

| 5%

| –4%

| –9%

| –1%

| –3%

| –6%

| –47%

| –39%

| 10%

|-
| colspan="2"| '''Total Aerosol ERF (W m''' <sup>–2</sup> ''')'''

2005–2014 rel. 1750

| 36%

| 37%

| 10%

| 16%

| 12%

| 0%

| 10%

| 8%

| 8%

| 38%

| 15%

| –31%

|-
| colspan="2"| '''GHG ERF (W m''' <sup>–2</sup> ''')'''

2019 rel. 1750

| 4%

| –5%

| –13%

| 1%

| 2%

| 1%

| 2%

| 1%

| –0%

| 1%

| 3%

| –3%

|-
| colspan="2"| '''Methane ERF (W m''' <sup>–2</sup> ''')'''

2019 rel. 1750

| 31%

| 4%

| –13%

| 3%

| 3%

| 3%

| 0%

| –0%

| 3%

| 8%

| –1%

| –5%

|-
| colspan="14"| '''Carbon Cycle metrics'''

|-
| colspan="2"| '''Airborne Fraction''' 1pctCO 2 '''(dimensionless)*'''

''2×CO'' <sub>2</sub>

|
| 8%

| –3%

| –11%

| 12%

| 6%

| –1%

| 1%

| –0%

| 8%

|-
| colspan="2"| '''Airborne Fraction''' 1pctCO 2 '''(dimensionless)*'''

''4×CO'' <sub>2</sub>

|
| 12%

| 1%

| –9%

| 15%

| 4%

| –6%

| 5%

| –1%

| –1%

|-
| colspan="14"| '''Future warming (GSAT) relative to 199''' '''5''' – '''2''' '''014'''

|-
| rowspan="3"| '''SSP1-1.9 (°C)'''

| 2021–2040

| 10%

| –4%

| 10%

| 3%

| 1%

| 11%

| 2%

| –0%

| 4%

| 12%

| –9%

| –25%

|-
| 2041–2060

| 8%

| –9%

| 7%

| –11%

| –8%

| 6%

| –1%

| –1%

| 7%

| 12%

| –8%

| –31%

|-
| 2081–2100

| –12%

| –25%

| –2%

| –25%

| –15%

| 4%

| –15%

| –8%

| 3%

| 7%

| –10%

| –31%

|-
| rowspan="3"| '''SSP1-2.6 (°C)'''

| 2021–2040

| 7%

| –5%

| 5%

| 2%

| 1%

| 8%

| –1%

| –2%

| –0%

| 9%

| –9%

| –28%

|-
| 2041–2060

| 8%

| –6%

| 2%

| –2%

| –2%

| 5%

| 0%

| 1%

| 2%

| 15%

| –6%

| –28%

|-
| 2081–2100

| –2%

| –14%

| –5%

| –8%

| –7%

| 1%

| –6%

| –1%

| 1%

| 17%

| –9%

| –29%

|-
| rowspan="3"| '''SSP2-4.5 (°C)'''

| 2021–2040

| 8%

| –5%

| 5%

| 7%

| –1%

| 2%

| 3%

| –3%

| –2%

| –5%

| –14%

| –30%

|-
| 2041–2060

| 4%

| –4%

| 3%

| 1%

| –1%

| 2%

| 1%

| 1%

| 2%

| 8%

| –8%

| –28%

|-
| 2081–2100

| –1%

| –10%

| –3%

| –2%

| –3%

| 1%

| –2%

| 1%

| 3%

| 8%

| –4%

| –25%

|-
| rowspan="3"| '''SSP3-7.0 (°C)'''

| 2021–2040

| 11%

| –4%

| 1%

| 14%

| 1%

| –1%

| 10%

| 1%

| –0%

| –5%

| –15%

| –29%

|-
| 2041–2060

| 4%

| –5%

| –0%

| 6%

| 0%

| –1%

| 7%

| 4%

| 1%

| 7%

| –8%

| –26%

|-
| 2081–2100

| –0%

| –8%

| –3%

| 3%

| –1%

| –1%

| 6%

| 3%

| 6%

| 5%

| –6%

| –25%

|-
| rowspan="3"| '''SSP5-8.5 (°C)'''

| 2021–2040

| 5%

| –7%

| 2%

| 9%

| 2%

| 4%

| 7%

| 1%

| 2%

| 1%

| –14%

| –30%

|-
| 2041–2060

| 2%

| –8%

| –1%

| 4%

| 0%

| 4%

| 3%

| 2%

| 4%

| 10%

| –6%

| –24%

|-
| 2081–2100

| 4%

| –7%

| –3%

| 6%

| –0%

| 1%

| 8%

| 4%

| 7%

| 9%

| –4%

| –25%

|}

'''Notes.''' Metrics calibrated against are equilibrium climate sensitivity, ECs ( [[#7.5|Section 7.5]] ); transient climate response to cumulative CO <sub>2</sub> emissions, TCRe ( [[IPCC:Wg1:Chapter:Chapter-5#5.5|Section 5.5]] ); transient climate response, TCr ( [[#7.5|Section 7.5]] ), historical GSAT change ( [[IPCC:Wg1:Chapter:Chapter-2#2.3|Section 2.3]] ); ocean heat uptake (Sections 7.2 and 2.3); effective radiative forcing, ERf ( [[#7.3|Section 7.3]] ); carbon cycle metrics, namely airborne fractions of idealized CO <sub>2</sub> scenarios (taking the ''likely'' range as twice the standard deviation across the models analysed in Arora et al. (2020; see also Table 5.7, ‘cross-AR6 lines of evidence’ row); and GSAT projections under the concentration-driven SSP scenarios for the near term (2021–2040), mid-term (2041–2060) and long term (2081–2100) relative to 1995–2014 (Table 4.2). See Supplementary Material, Table 7.SM.4 for a version of this table with the absolute values rather than percentage differences. The columns labelled ‘upper’ and ‘lower’ indicate 5–95% ranges, except for the variables demarcated with an asterisk or double asterisk (* or **), where they denote ''likely'' ranges from 17–83%. Note that the TCRE assessed range (**) is wider than the combination of the TCR and airborne fraction to account for uncertainties related to model limitations (Table 5.7) hence it is expected that the emulators are too narrow on this particular metric and/or too wide on TCR and airborne fraction. For illustrative purposes, the cells are coloured as follows: white cells indicate small differences (up to ±5% for the central value and +10% for the ranges), light blue and light yellow cells indicate medium differences (up to +10% and –10% for light blue and light yellow for central values, respectively; up to ±20% for the ranges) and darker cells indicate larger positive (blue) or negative (yellow) differences. Note that values are rounded after the colours are applied.

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