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

=== 7.3.5 Synthesis of Global Mean Radiative Forcing, Past and Future ===

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<span id="major-changes-in-forcing-since-the-ipcc-fifth-assessment-report"></span>
==== 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> .

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<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]] .

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[[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).

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

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'''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]] ).

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[[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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