IPCC:AR6/WGI/Chapter-6 - Revision history

Laura: /* References */

2026-06-24T09:54:35Z

References

← Older revision		Revision as of 09:54, 24 June 2026
Line 6,162:		Line 6,162:
	Zusman, E. et al., 2013: Co-benefits: Taking a multidisciplinary approach. ''Carbon Management'' , '''4(2)''' , 135–137, doi: [https://dx.doi.org/10.4155/cmt.13.12 10.4155/cmt.13.12] .		Zusman, E. et al., 2013: Co-benefits: Taking a multidisciplinary approach. ''Carbon Management'' , '''4(2)''' , 135–137, doi: [https://dx.doi.org/10.4155/cmt.13.12 10.4155/cmt.13.12] .

	~~-----~~		== Footnotes ==

	<div id="footnote-004" class="_idFootnote"></div>		<div id="footnote-004" class="_idFootnote"></div>

Laura: /* 6.6.3.3 Assessment of SLCF Mitigation Strategies and Opportunities */

2026-06-24T09:52:56Z

6.6.3.3 Assessment of SLCF Mitigation Strategies and Opportunities

← Older revision		Revision as of 09:52, 24 June 2026
Line 2,397:		Line 2,397:

	<div id="box-6.2" class="h2-container box-container"></div>		<div id="box-6.2" class="h2-container box-container"></div>
			<div class="container-box col-regular">
	~~'''~~Box 6.2 \| SLCF Mitigation and Sustainable Development Goals (SDG) Opportunities~~'''~~		== Box 6.2 \| SLCF Mitigation and Sustainable Development Goals (SDG) Opportunities ==

	<div id="h2-31-siblings" class="h2-siblings"></div>		<div id="h2-31-siblings" class="h2-siblings"></div>
Line 2,407:		Line 2,407:

	In addition, pursuing SDG objectives, apparently decoupled from air pollution, such as improved waste management, access to clean energy, or improved agricultural practices, would also stimulate and lead to mitigation of SLCFs. Amann et al. (2020) show that a global strategy to achieve the WHO air quality guidelines, cannot only rely on air pollution control but also on a combination of SDG-aligned policies. Such actions would include energy efficiency improvements, increased use of renewables, reduction of methane from waste management and agriculture, and CO <sub>2</sub> and methane due to lower fossil fuel consumption, resulting in climate co-benefits. Consideration of SDGs including local air-quality co-benefits, creates an opportunity to support and gain acceptance for ambitious climate change mitigation ([[#Jakob--2016\|Jakob and Steckel, 2016]] ; [[#Stechow--2016\|Stechow et al., 2016]] ; [[#Vandyck--2018\|Vandyck et al., 2018]]). Such near-term policies targeting SDGs and air quality would enable longer-term transformations necessary to achieve climate goals (Chapter 17, WGIII).		In addition, pursuing SDG objectives, apparently decoupled from air pollution, such as improved waste management, access to clean energy, or improved agricultural practices, would also stimulate and lead to mitigation of SLCFs. Amann et al. (2020) show that a global strategy to achieve the WHO air quality guidelines, cannot only rely on air pollution control but also on a combination of SDG-aligned policies. Such actions would include energy efficiency improvements, increased use of renewables, reduction of methane from waste management and agriculture, and CO <sub>2</sub> and methane due to lower fossil fuel consumption, resulting in climate co-benefits. Consideration of SDGs including local air-quality co-benefits, creates an opportunity to support and gain acceptance for ambitious climate change mitigation ([[#Jakob--2016\|Jakob and Steckel, 2016]] ; [[#Stechow--2016\|Stechow et al., 2016]] ; [[#Vandyck--2018\|Vandyck et al., 2018]]). Such near-term policies targeting SDGs and air quality would enable longer-term transformations necessary to achieve climate goals (Chapter 17, WGIII).
			</div>
	In summary, there is ''high confidence'' that effective decarbonization strategies could lead to air-quality improvements but are not sufficient to achieve, in the near term, air-quality WHO guideline values set for fine particulate matter, especially in parts of Asia and in some highly polluted regions. Additional policies (e.g., access to clean energy, waste management) envisaged to attain SDGs bring complementary SLCF reduction (''high confidence)'' . Sustained methane mitigation, wherever it occurs, stands out as an option that combines near- and long-term gains on surface temperature (''high confidence'') and leads to an air pollution benefit by reducing ozone levels globally (''high confidence'') ''.''		In summary, there is ''high confidence'' that effective decarbonization strategies could lead to air-quality improvements but are not sufficient to achieve, in the near term, air-quality WHO guideline values set for fine particulate matter, especially in parts of Asia and in some highly polluted regions. Additional policies (e.g., access to clean energy, waste management) envisaged to attain SDGs bring complementary SLCF reduction (''high confidence)'' . Sustained methane mitigation, wherever it occurs, stands out as an option that combines near- and long-term gains on surface temperature (''high confidence'') and leads to an air pollution benefit by reducing ozone levels globally (''high confidence'') ''.''

	<div id="cross-chapter-box-6.1" class="h2-container box-container"></div>		<div id="cross-chapter-box-6.1" class="h2-container box-container"></div>
			<div class="container-box col-cross">
	~~'''~~Cross-Chapter Box 6.1 \| Implications of COVID-19 Restrictions for Emissions, Air Quality and Climate~~'''~~		== Cross-Chapter Box 6.1 \| Implications of COVID-19 Restrictions for Emissions, Air Quality and Climate ==

	<div id="h2-32-siblings" class="h2-siblings"></div>		<div id="h2-32-siblings" class="h2-siblings"></div>
Line 2,446:		Line 2,446:
	[[#Forster--2020\|Forster et al. (2020)]] combined the FaIR emulator (Cross-Chapter Box 7.1) with emissions changes for a range of species, relative to a continuation of Nationally Determined Contributions ([[#Rogelj--2017\|Rogelj et al., 2017]]). They found a negative ERF from avoided CO <sub>2</sub> emissions that strengthens through 2020 to –0.01 W m <sup>–2</sup> . During the spring lockdown, they found a peak positive ERF of 0.1 W m <sup>–2</sup> from loss of aerosol-induced cooling, and a peak negative ERF of –0.04 W m <sup>–2</sup> from reductions in tropospheric ozone (from reduced photochemical production via NO <sub>x</sub>). Overall, they estimated a net ERF of +0.05 W m <sup>–2</sup> for spring 2020, declining to +0.025 W m <sup>–2</sup> by the end of the year.		[[#Forster--2020\|Forster et al. (2020)]] combined the FaIR emulator (Cross-Chapter Box 7.1) with emissions changes for a range of species, relative to a continuation of Nationally Determined Contributions ([[#Rogelj--2017\|Rogelj et al., 2017]]). They found a negative ERF from avoided CO <sub>2</sub> emissions that strengthens through 2020 to –0.01 W m <sup>–2</sup> . During the spring lockdown, they found a peak positive ERF of 0.1 W m <sup>–2</sup> from loss of aerosol-induced cooling, and a peak negative ERF of –0.04 W m <sup>–2</sup> from reductions in tropospheric ozone (from reduced photochemical production via NO <sub>x</sub>). Overall, they estimated a net ERF of +0.05 W m <sup>–2</sup> for spring 2020, declining to +0.025 W m <sup>–2</sup> by the end of the year.

	~~Cross-Chapter Box 6.1~~

	[[File:e5663caf3f762ec596b8100c7e22007c IPCC_AR6_WGI_CCBox_6_1_Figure_1.png]]		[[File:e5663caf3f762ec596b8100c7e22007c IPCC_AR6_WGI_CCBox_6_1_Figure_1.png]]

	'''Cross-Chapter Box 6.1, Figure 1'''		'''Cross-Chapter Box 6.1, Figure 1''' '''\|''' '''Emissions reductions and their effect on aerosols and climate in response to COVID-19.''' Estimated reductions in emissions of CO <sub>2</sub> , SO <sub>2</sub> and NO <sub>x</sub> are shown in panel '''(a)''' based on reconstructions using activity data (updated from [[#Forster--2020\|Forster et al., 2020]]). Eight Earth system models (ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until August 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. Panel '''(b)''' shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel '''(c)''' shows ESM-simulated GSAT anomalies during 2020; curves denote the ensemble mean result for each model with shading used for ±1 standard deviation for each model. ESM data from these simulations (‘ssp245-covid’) is archived on the Earth System Grid CMIP6 database. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1.

	'''\|'''

	'''Emissions reductions and their effect on aerosols and climate in response to COVID-19.''' Estimated reductions in emissions of CO <sub>2</sub> , SO <sub>2</sub> and NO <sub>x</sub> are shown in panel '''(a)''' based on reconstructions using activity data (updated from [[#Forster--2020\|Forster et al., 2020]]). Eight Earth system models (ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until August 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. Panel '''(b)''' shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel '''(c)''' shows ESM-simulated GSAT anomalies during 2020; curves denote the ensemble mean result for each model with shading used for ±1 standard deviation for each model. ESM data from these simulations (‘ssp245-covid’) is archived on the Earth System Grid CMIP6 database. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1.
	[[#Gettelman--2021\|Gettelman et al. (2021)]] extended Forster et al.’s (2020) results using two ESMs, and found a spring peak aerosol-induced ERF ranging from 0.12 to 0.3 W m <sup>–2</sup> , depending on the aerosol parametrization. They also estimated an ERF of –0.04 W m <sup>–2</sup> from loss of contrail warming. Overall, they report a peak ERF of 0.04 to 0.2 W m <sup>–2</sup> , and a subsequent decline to around half the peak value. Two independent ESM studies [[#Weber--2020\|Weber et al. (2020)]] and [[#Yang--2020\|Yang et al. (2020)]] found consistent results in time evolution and component contributions but included fewer forcing components.		[[#Gettelman--2021\|Gettelman et al. (2021)]] extended Forster et al.’s (2020) results using two ESMs, and found a spring peak aerosol-induced ERF ranging from 0.12 to 0.3 W m <sup>–2</sup> , depending on the aerosol parametrization. They also estimated an ERF of –0.04 W m <sup>–2</sup> from loss of contrail warming. Overall, they report a peak ERF of 0.04 to 0.2 W m <sup>–2</sup> , and a subsequent decline to around half the peak value. Two independent ESM studies [[#Weber--2020\|Weber et al. (2020)]] and [[#Yang--2020\|Yang et al. (2020)]] found consistent results in time evolution and component contributions but included fewer forcing components.

Line 2,462:		Line 2,457:

	Changes in atmospheric composition due to COVID-19 emissions reductions are not thought to have caused a detectable change in global temperature or rainfall in 2020 (''high confidence''). A large ensemble of Earth system model (ESM) simulations show an ensemble average reduction in Aerosol Optical Depth (AOD) in some regions, notably Eastern and Southern Asia ([[#Fyfe--2021\|Fyfe et al., 2021]]). This result is supported by observational studies finding decreases in optical depth in 2020 ([[#Gkatzelis--2021\|Gkatzelis et al., 2021]] ; [[#Ming--2021\|Ming et al., 2021]] ; [[#van%20Heerwaarden--2021\|van Heerwaarden et al., 2021]]), which may have contributed to observed increases in solar irradiance ([[#van%20Heerwaarden--2021\|van Heerwaarden et al., 2021]]) or solar clear-sky reflection ([[#Ming--2021\|Ming et al., 2021]]).		Changes in atmospheric composition due to COVID-19 emissions reductions are not thought to have caused a detectable change in global temperature or rainfall in 2020 (''high confidence''). A large ensemble of Earth system model (ESM) simulations show an ensemble average reduction in Aerosol Optical Depth (AOD) in some regions, notably Eastern and Southern Asia ([[#Fyfe--2021\|Fyfe et al., 2021]]). This result is supported by observational studies finding decreases in optical depth in 2020 ([[#Gkatzelis--2021\|Gkatzelis et al., 2021]] ; [[#Ming--2021\|Ming et al., 2021]] ; [[#van%20Heerwaarden--2021\|van Heerwaarden et al., 2021]]), which may have contributed to observed increases in solar irradiance ([[#van%20Heerwaarden--2021\|van Heerwaarden et al., 2021]]) or solar clear-sky reflection ([[#Ming--2021\|Ming et al., 2021]]).

	~~Cross-Chapter Box 6.1~~

	Model simulations of the response to COVID-19 emissions reductions indicate a small warming of global surface air temperature (GSAT) due to a decrease in sulphate aerosols ([[#Forster--2020\|Forster et al., 2020]] ; [[#Fyfe--2021\|Fyfe et al., 2021]]), balanced by cooling due to an ozone decrease ([[#Forster--2020\|Forster et al., 2020]] ; [[#Weber--2020\|Weber et al., 2020]]), black carbon decrease ([[#Weber--2020\|Weber et al., 2020]]) and CO <sub>2</sub> decrease. It is noted that observational studies report little SO <sub>2</sub> change, at least locally near the surface ([[#Shi--2021\|Shi et al., 2021]]), and do not correlate with emissions inventory-based changes ([[#Gkatzelis--2021\|Gkatzelis et al., 2021]]). One study suggests a small net warming while another using idealized simulations suggests a small cooling ([[#Weber--2020\|Weber et al., 2020]]). Simulated GSAT and rainfall changes are unlikely to be detectable in observations (''high confidence'') ([[#Samset--2020\|Samset et al., 2020]] ; [[#Fyfe--2021\|Fyfe et al., 2021]]). Multi-model ESM simulations based on a realistic COVID-19 containment forcing scenario ([[#Forster--2020\|Forster et al., 2020]]) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross-Chapter Box 6.1).		Model simulations of the response to COVID-19 emissions reductions indicate a small warming of global surface air temperature (GSAT) due to a decrease in sulphate aerosols ([[#Forster--2020\|Forster et al., 2020]] ; [[#Fyfe--2021\|Fyfe et al., 2021]]), balanced by cooling due to an ozone decrease ([[#Forster--2020\|Forster et al., 2020]] ; [[#Weber--2020\|Weber et al., 2020]]), black carbon decrease ([[#Weber--2020\|Weber et al., 2020]]) and CO <sub>2</sub> decrease. It is noted that observational studies report little SO <sub>2</sub> change, at least locally near the surface ([[#Shi--2021\|Shi et al., 2021]]), and do not correlate with emissions inventory-based changes ([[#Gkatzelis--2021\|Gkatzelis et al., 2021]]). One study suggests a small net warming while another using idealized simulations suggests a small cooling ([[#Weber--2020\|Weber et al., 2020]]). Simulated GSAT and rainfall changes are unlikely to be detectable in observations (''high confidence'') ([[#Samset--2020\|Samset et al., 2020]] ; [[#Fyfe--2021\|Fyfe et al., 2021]]). Multi-model ESM simulations based on a realistic COVID-19 containment forcing scenario ([[#Forster--2020\|Forster et al., 2020]]) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross-Chapter Box 6.1).
			</div>
	<div id="6.7" class="h1-container"></div>		<div id="6.7" class="h1-container"></div>

	<span id="future-projections-of-atmospheric-composition-and-climate-response-in-ssp-scenarios"></span>		<span id="future-projections-of-atmospheric-composition-and-climate-response-in-ssp-scenarios"></span>

	== 6.7 Future Projections of Atmospheric Composition and Climate Response in SSP Scenarios ==		== 6.7 Future Projections of Atmospheric Composition and Climate Response in SSP Scenarios ==

Laura: /* 6.3 Evolution of Atmospheric SLCF Abundances */

2026-06-24T09:49:48Z

6.3 Evolution of Atmospheric SLCF Abundances

← Older revision		Revision as of 09:49, 24 June 2026
Line 655:		Line 655:

	<div id="box-6.1" class="h2-container box-container"></div>		<div id="box-6.1" class="h2-container box-container"></div>
			<div class="container-box col-regular">
	~~'''~~Box 6.1 \| Atmospheric Abundance of SLCFs: From Process-level Studies to Global Chemistry–Climate Models~~'''~~		== Box 6.1 \| Atmospheric Abundance of SLCFs: From Process-level Studies to Global Chemistry–Climate Models ==

	<div id="h2-12-siblings" class="h2-siblings"></div>		<div id="h2-12-siblings" class="h2-siblings"></div>
Line 676:		Line 676:
	'''Knowledge exchange between laboratory/theoretical studies, observations and global climate–chemistry models (CCMs) to inform our understanding of short-lived climate forcers (SLCFs).'''		'''Knowledge exchange between laboratory/theoretical studies, observations and global climate–chemistry models (CCMs) to inform our understanding of short-lived climate forcers (SLCFs).'''
	model parametrizations (e.g., chemical mechanisms, photolysis schemes, parametrizations for mixing and convective transport, and deposition), model input parameters (e.g., reaction rate constants, emissions) and an incomplete understanding of the physical and chemical processes that determine SLCF distributions ([[#Brasseur--2017\|Brasseur and Jacob, 2017]] ; [[#Young--2018\|Young et al., 2018]]). CCMs can therefore not capture every aspect of atmospheric chemical composition, but are expected to represent, as faithfully as possible, the sensitivity of chemical compounds to their drivers (e.g., anthropogenic emissions). Models are evaluated in multiple ways to identify their strengths and weaknesses in explaining the evolution of SLCF abundances. For example, CCM simulations are performed in the nudged or offline meteorology mode, that is, driven by observed or reanalysed meteorology rather than in the free-running mode, for consistent comparison of modelled chemical composition with observations for a specific time period ([[#Dameris--2013\|Dameris and Jöckel, 2013]]). However, caution is exercised as nudging can alter the model climate resulting in unintentional impacts on the simulated atmospheric physics and/or chemistry ([[#Orbe--2018\|Orbe et al., 2018]] ; [[#Chrysanthou--2019\|Chrysanthou et al., 2019]]). Chemical mechanisms implemented in CCMs are evaluated and intercompared to assess their skill in capturing relevant chemistry features (e.g., [[#Brown-Steiner--2018\|Brown-Steiner et al., 2018]]). The multi-model ensemble approach, employed for evaluating climate models, has been particularly useful for characterizing errors in CCM simulations of SLCFs related to structural uncertainty and internal variability (Naik et al. , 2013; Shindell et al. , 2013; Young et al. , 2013; Turnock et al. , 2020) . However, as discussed in Box 4.1, this approach is unable to capture the full uncertainty range.		model parametrizations (e.g., chemical mechanisms, photolysis schemes, parametrizations for mixing and convective transport, and deposition), model input parameters (e.g., reaction rate constants, emissions) and an incomplete understanding of the physical and chemical processes that determine SLCF distributions ([[#Brasseur--2017\|Brasseur and Jacob, 2017]] ; [[#Young--2018\|Young et al., 2018]]). CCMs can therefore not capture every aspect of atmospheric chemical composition, but are expected to represent, as faithfully as possible, the sensitivity of chemical compounds to their drivers (e.g., anthropogenic emissions). Models are evaluated in multiple ways to identify their strengths and weaknesses in explaining the evolution of SLCF abundances. For example, CCM simulations are performed in the nudged or offline meteorology mode, that is, driven by observed or reanalysed meteorology rather than in the free-running mode, for consistent comparison of modelled chemical composition with observations for a specific time period ([[#Dameris--2013\|Dameris and Jöckel, 2013]]). However, caution is exercised as nudging can alter the model climate resulting in unintentional impacts on the simulated atmospheric physics and/or chemistry ([[#Orbe--2018\|Orbe et al., 2018]] ; [[#Chrysanthou--2019\|Chrysanthou et al., 2019]]). Chemical mechanisms implemented in CCMs are evaluated and intercompared to assess their skill in capturing relevant chemistry features (e.g., [[#Brown-Steiner--2018\|Brown-Steiner et al., 2018]]). The multi-model ensemble approach, employed for evaluating climate models, has been particularly useful for characterizing errors in CCM simulations of SLCFs related to structural uncertainty and internal variability (Naik et al. , 2013; Shindell et al. , 2013; Young et al. , 2013; Turnock et al. , 2020) . However, as discussed in Box 4.1, this approach is unable to capture the full uncertainty range.
			</div>

	This assessment draws upon results from single-model studies and recent multi-model intercomparisons (e.g., AeroCom, CCMI), in particular those endorsed by CMIP6 (see Table 1.3), which then allows for the full consideration of robustness and uncertainty due to model structures and processes. Based on the collective information provided in this body of literature, the CMIP6 multi-model ensemble is largely fit-for-purpose of evaluating the influence of SLCFs on radiative forcing, climate and non-CO <sub>2</sub> biogeochemical feedbacks. Additionally, CMIP6 models are fit for capturing the global air pollution response to changes in emissions and meteorology, but have difficulty in simulating the mean state ([[#Turnock--2020\|Turnock et al., 2020]]). The set of CMIP6 simulations has been used to update the relations between emissions and surface temperature at the heart of the emulators (Cross-Chapter Box 7.1) and update emissions metrics ([[IPCC:Wg1:Chapter:Chapter-7#7.6\|Section 7.6]]). Emulators and emissions metrics are used in this chapter (Sections 6.6 and 6.7) to assess more specifically the effect of the individual SLCFs for each sector and region, which would be of prohibitive computing cost with CCMs. CCMs are also used to build global source-receptor models which use relations between surface concentrations and emissions. Such a model is used to assess the impact of various mitigation policies on air quality (Sections 6.5 and 6.7).		This assessment draws upon results from single-model studies and recent multi-model intercomparisons (e.g., AeroCom, CCMI), in particular those endorsed by CMIP6 (see Table 1.3), which then allows for the full consideration of robustness and uncertainty due to model structures and processes. Based on the collective information provided in this body of literature, the CMIP6 multi-model ensemble is largely fit-for-purpose of evaluating the influence of SLCFs on radiative forcing, climate and non-CO <sub>2</sub> biogeochemical feedbacks. Additionally, CMIP6 models are fit for capturing the global air pollution response to changes in emissions and meteorology, but have difficulty in simulating the mean state ([[#Turnock--2020\|Turnock et al., 2020]]). The set of CMIP6 simulations has been used to update the relations between emissions and surface temperature at the heart of the emulators (Cross-Chapter Box 7.1) and update emissions metrics ([[IPCC:Wg1:Chapter:Chapter-7#7.6\|Section 7.6]]). Emulators and emissions metrics are used in this chapter (Sections 6.6 and 6.7) to assess more specifically the effect of the individual SLCFs for each sector and region, which would be of prohibitive computing cost with CCMs. CCMs are also used to build global source-receptor models which use relations between surface concentrations and emissions. Such a model is used to assess the impact of various mitigation policies on air quality (Sections 6.5 and 6.7).
Line 1,834:		Line 1,835:

	<span id="slcf-radiative-forcing-and-climate-effects"></span>		<span id="slcf-radiative-forcing-and-climate-effects"></span>

	== 6.4 SLCF Radiative Forcing and Climate Effects ==		== 6.4 SLCF Radiative Forcing and Climate Effects ==

Laura: /* Potential Effects of SLCF Mitigation */

2026-06-24T09:48:12Z

Potential Effects of SLCF Mitigation

← Older revision		Revision as of 09:48, 24 June 2026
Line 115:		Line 115:
	<div id="h2-5-siblings" class="h2-siblings"></div>		<div id="h2-5-siblings" class="h2-siblings"></div>

	'''Over time scales of 10 to 20 years, the global temperature response to a year’s worth of current emissions of SLCFs is at least as large as that due to a year’s worth of CO~~'''~~ <sub>2</sub> ~~'''~~emissions~~'''~~ (''high confidence~~'' '~~''). Sectors producing the largest SLCF-induced warming are those dominated by methane emissions: fossil fuel production and distribution, agriculture and waste management~~'''~~ (''high confidence~~'' '~~'').''' On these time scales, SLCFs with cooling effects can significantly mask the CO <sub>2</sub> warming in the case of fossil fuel combustion for energy and land transportation, or completely offset the CO <sub>2</sub> warming and lead to an overall net cooling in the case of industry and maritime shipping (prior to the implementation of the revised fuel-sulphur limit policy for shipping in 2020) (''medium confidence''). Ten years after a one-year pulse of present-day aviation emissions, SLCFs induce strong but short-lived warming contributions to the GSAT response (''medium confidence''), while CO <sub>2</sub> both gives a warming effect in the near term and dominates the long-term warming impact (''high-confidence''). {6.6.1, 6.6.2}		'''Over time scales of 10 to 20 years, the global temperature response to a year’s worth of current emissions of SLCFs is at least as large as that due to a year’s worth of CO<sub>2</sub> emissions (''high confidence''). Sectors producing the largest SLCF-induced warming are those dominated by methane emissions: fossil fuel production and distribution, agriculture and waste management (''high confidence'').''' On these time scales, SLCFs with cooling effects can significantly mask the CO <sub>2</sub> warming in the case of fossil fuel combustion for energy and land transportation, or completely offset the CO <sub>2</sub> warming and lead to an overall net cooling in the case of industry and maritime shipping (prior to the implementation of the revised fuel-sulphur limit policy for shipping in 2020) (''medium confidence''). Ten years after a one-year pulse of present-day aviation emissions, SLCFs induce strong but short-lived warming contributions to the GSAT response (''medium confidence''), while CO <sub>2</sub> both gives a warming effect in the near term and dominates the long-term warming impact (''high-confidence''). {6.6.1, 6.6.2}

	'''The effects of SLCFs decay rapidly over the first few decades after pulse emission. Consequently, on time scales longer than about 30 years, the net long-term global temperature effects of sectors and regions are dominated by CO~~'''~~ <sub>2</sub> (''high confidence~~'' '~~'').''' The global mean temperature response following a climate change mitigation measure that affects emissions of both short- and long-lived climate forcers depends on their atmospheric decay times, how fast and for how long the emissions are reduced, and the inertia in the climate system. For SLCFs including methane, the rate of emissions drives the long-term global temperature effect, as opposed to CO <sub>2</sub> for which the long-term global temperature effect is controlled by the cumulative emissions. About 30 years or more after a one-year emission pulse occurs, the sectors contributing the most to global warming are industry, fossil fuel combustion for energy and land transportation, essentially through CO <sub>2</sub> (''high confidence''). Current emissions of SLCFs, CO <sub>2</sub> and N <sub>2</sub> O from Eastern Asia and North America are the largest regional contributors to additional net future warming on both short (''medium confidence'') and long time scales (''high confidence''). {6.6.1, 6.6.2}		'''The effects of SLCFs decay rapidly over the first few decades after pulse emission. Consequently, on time scales longer than about 30 years, the net long-term global temperature effects of sectors and regions are dominated by CO<sub>2</sub> (''high confidence'').''' The global mean temperature response following a climate change mitigation measure that affects emissions of both short- and long-lived climate forcers depends on their atmospheric decay times, how fast and for how long the emissions are reduced, and the inertia in the climate system. For SLCFs including methane, the rate of emissions drives the long-term global temperature effect, as opposed to CO <sub>2</sub> for which the long-term global temperature effect is controlled by the cumulative emissions. About 30 years or more after a one-year emission pulse occurs, the sectors contributing the most to global warming are industry, fossil fuel combustion for energy and land transportation, essentially through CO <sub>2</sub> (''high confidence''). Current emissions of SLCFs, CO <sub>2</sub> and N <sub>2</sub> O from Eastern Asia and North America are the largest regional contributors to additional net future warming on both short (''medium confidence'') and long time scales (''high confidence''). {6.6.1, 6.6.2}

	'''At present, emissions from the residential and commercial sectors (fossil and biofuel use for cooking and heating) and the energy sector (fossil fuel production, distribution and combustion) contribute the most to the world population’s exposure to anthropogenic fine PM~~'''~~ (''high confidence~~'' '~~''), whereas emissions from the energy and land transportation sectors contribute the most to ozone exposure~~'''~~ (''medium to'' ''high confidence~~'' '~~'').''' The contribution of different sectors to PM varies across regions, with the residential sector being the most important in Southern Asia and Africa, agricultural emissions dominating in Europe and North America, and industry and energy production dominating in Central and Eastern Asia, Latin America and the Middle East. Energy and industry are important PM <sub>2.5</sub> contributors in most regions, except Africa (''high confidence''). Sector contributions to surface ozone concentrations are similar for all regions. {6.6.2}		'''At present, emissions from the residential and commercial sectors (fossil and biofuel use for cooking and heating) and the energy sector (fossil fuel production, distribution and combustion) contribute the most to the world population’s exposure to anthropogenic fine PM (''high confidence''), whereas emissions from the energy and land transportation sectors contribute the most to ozone exposure (''medium'' to ''high confidence'').''' The contribution of different sectors to PM varies across regions, with the residential sector being the most important in Southern Asia and Africa, agricultural emissions dominating in Europe and North America, and industry and energy production dominating in Central and Eastern Asia, Latin America and the Middle East. Energy and industry are important PM <sub>2.5</sub> contributors in most regions, except Africa (''high confidence''). Sector contributions to surface ozone concentrations are similar for all regions. {6.6.2}

	'''Assuming implementation and efficient enforcement of both the Kigali Amendment to the Montreal Protocol on Ozone Depleting Substances and current national plans to limit emissions (as in~~''' '''~~SSP1-2.6~~''' '''~~), the effects of HFCs on GSAT, relative to 2019, would remain below +0.02°C from 2050 onwards versus about +0.04°C to +0.08°C in 2050 and +0.1°C to +0.3°C in 2100 considering only national HFC regulations decided prior to the Kigali Amendment (as in~~''' '''~~SSP5-8.5~~''' '''~~)~~'''~~ (''medium confidence~~'' '~~'').''' Further improvements in the efficiency of refrigeration and air-conditioning equipment during the transition to low-global-warming-potential refrigerants would bring additional greenhouse gas reductions (''medium confidence'') resulting in benefits for climate change mitigation and to a lesser extent for air quality due to reduced air pollutant emissions from power plants . {6.6.3, 6.7.3}		'''Assuming implementation and efficient enforcement of both the Kigali Amendment to the Montreal Protocol on Ozone Depleting Substances and current national plans to limit emissions (as in SSP1-2.6), the effects of HFCs on GSAT, relative to 2019, would remain below +0.02°C from 2050 onwards versus about +0.04°C to +0.08°C in 2050 and +0.1°C to +0.3°C in 2100 considering only national HFC regulations decided prior to the Kigali Amendment (as in SSP5-8.5 ) (''medium confidence'').''' Further improvements in the efficiency of refrigeration and air-conditioning equipment during the transition to low-global-warming-potential refrigerants would bring additional greenhouse gas reductions (''medium confidence'') resulting in benefits for climate change mitigation and to a lesser extent for air quality due to reduced air pollutant emissions from power plants . {6.6.3, 6.7.3}

	'''Future changes in SLCFs are expected to cause additional warming. This warming is stable after 2040 in scenarios leading to lower global air pollution as long as methane emissions are also mitigated, but the overall warming induced by SLCF changes is higher in scenarios in which air quality continues to deteriorate (induced by growing fossil fuel use and limited air pollution control)~~'''~~ (''high confidence~~'' '~~'').''' If a strong air pollution control resulting in reductions in anthropogenic aerosols and non-methane ozone precursors was considered in SSP3-7.0 , it would lead to a ''likely'' additional near-term global warming of 0.08 [0.00 to 0.10] °C in 2040. An additional concomitant methane mitigation (consistent with SSP1’s stringent climate change mitigation policy implemented in the SSP3 world) would not only alleviate this warming but would turn this into a cooling of 0.07 °C with a ''likely'' range of [–0.02 to +0.14] °C (compared with SSP3-7.0 in 2040) . Across the SSPs, the collective reduction of methane, ozone precursors and HFCs can make a difference of 0.2°C with a ''very likely'' range of [0.1 to 0.4] °C in 2040 and 0.8°C with a ''very likely'' range of [0.5 to 1.3] °C at the end of the 21 st century (comparing SSP3-7.0 and SSP1-1.9), which is substantial in the context of the Paris Agreement. Sustained methane mitigation, wherever it occurs, stands out as an option that combines near- and long-term gains on surface temperature (''high confidence'') and leads to air-quality benefits by reducing surface ozone levels globally (''high confidence''). {6.6.3, 6.7.3, 4.4.4}		'''Future changes in SLCFs are expected to cause additional warming. This warming is stable after 2040 in scenarios leading to lower global air pollution as long as methane emissions are also mitigated, but the overall warming induced by SLCF changes is higher in scenarios in which air quality continues to deteriorate (induced by growing fossil fuel use and limited air pollution control) (''high confidence'').''' If a strong air pollution control resulting in reductions in anthropogenic aerosols and non-methane ozone precursors was considered in SSP3-7.0 , it would lead to a ''likely'' additional near-term global warming of 0.08 [0.00 to 0.10] °C in 2040. An additional concomitant methane mitigation (consistent with SSP1’s stringent climate change mitigation policy implemented in the SSP3 world) would not only alleviate this warming but would turn this into a cooling of 0.07 °C with a ''likely'' range of [–0.02 to +0.14] °C (compared with SSP3-7.0 in 2040) . Across the SSPs, the collective reduction of methane, ozone precursors and HFCs can make a difference of 0.2°C with a ''very likely'' range of [0.1 to 0.4] °C in 2040 and 0.8°C with a ''very likely'' range of [0.5 to 1.3] °C at the end of the 21 st century (comparing SSP3-7.0 and SSP1-1.9), which is substantial in the context of the Paris Agreement. Sustained methane mitigation, wherever it occurs, stands out as an option that combines near- and long-term gains on surface temperature (''high confidence'') and leads to air-quality benefits by reducing surface ozone levels globally (''high confidence''). {6.6.3, 6.7.3, 4.4.4}

	'''Rapid decarbonization strategies lead to air-quality improvements but are not sufficient to achieve, in the near term, air-quality guidelines set for fine PM by the World Health Organization (WHO), especially in parts of Asia and in some other highly polluted regions~~'''~~ (''high confidence~~'' '~~'').''' Additional methane and BC mitigation would contribute to offsetting the additional warming associated with SO <sub>2</sub> reductions that would accompany decarbonization (''high confidence''). Strong air pollution control as well as strong climate change mitigation, implemented separately, lead to large reductions in exposure to air pollution by the end of the century (''high confidence''). Implementation of air pollution controls, relying on the deployment of existing technologies, leads more rapidly to air quality benefits than climate change mitigation (''high confidence''), which requires systemic changes. However, in both cases, significant parts of the population are projected to remain exposed to air pollution exceeding the WHO guidelines (''high confidence''). Additional policies envisaged to attain Sustainable Development Goals (SDGs; e.g., access to clean energy, waste management) bring complementary SLCF reduction. Only strategies integrating climate, air quality, and development goals are found to effectively achieve multiple benefits. {6.6.3, 6.7.3, Box 6.2}		'''Rapid decarbonization strategies lead to air-quality improvements but are not sufficient to achieve, in the near term, air-quality guidelines set for fine PM by the World Health Organization (WHO), especially in parts of Asia and in some other highly polluted regions (''high confidence'').''' Additional methane and BC mitigation would contribute to offsetting the additional warming associated with SO <sub>2</sub> reductions that would accompany decarbonization (''high confidence''). Strong air pollution control as well as strong climate change mitigation, implemented separately, lead to large reductions in exposure to air pollution by the end of the century (''high confidence''). Implementation of air pollution controls, relying on the deployment of existing technologies, leads more rapidly to air quality benefits than climate change mitigation (''high confidence''), which requires systemic changes. However, in both cases, significant parts of the population are projected to remain exposed to air pollution exceeding the WHO guidelines (''high confidence''). Additional policies envisaged to attain Sustainable Development Goals (SDGs; e.g., access to clean energy, waste management) bring complementary SLCF reduction. Only strategies integrating climate, air quality, and development goals are found to effectively achieve multiple benefits. {6.6.3, 6.7.3, Box 6.2}

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

	<span id="implications-of-covid-19-restrictions-for-emissions-air-quality-and-climate"></span>		<span id="implications-of-covid-19-restrictions-for-emissions-air-quality-and-climate"></span>

	=== Implications of COVID-19 Restrictions for Emissions, Air Quality and Climate ===		=== Implications of COVID-19 Restrictions for Emissions, Air Quality and Climate ===

Laura: /* Future Projections of the Effect of SLCFs on GSAT in the Core SSPs */

2026-06-24T09:46:26Z

Future Projections of the Effect of SLCFs on GSAT in the Core SSPs

← Older revision		Revision as of 09:46, 24 June 2026
Line 103:		Line 103:
	<div id="h2-4-siblings" class="h2-siblings"></div>		<div id="h2-4-siblings" class="h2-siblings"></div>

	'''In the next two decades, it is~~'''~~ ''very likely~~'' '~~''that the SLCF emissions changes in the WGI core set of SSPs will cause a warming relative to 2019, whatever the SSPs, in addition to the warming from long-lived greenhouse gases. The net effect of SLCF and hydrofluorocarbon~~'''~~ (~~'''~~HFC) changes on GSAT across the SSPs~~''' '''~~is a~~'''~~ ''likely~~'' '~~''warming of 0.06°C–0.35°C in 2040 relative to 2019. Warming over the next two decades is quite similar across the SSPs due to competing effects of warming (methane, ozone) and cooling (aerosols) SLCFs''' . For the scenarios with the most stringent climate and air pollution mitigations (SSP1-1.9 and SSP1-2.6), the ''likely'' near-term warming from the SLCFs is predominantly due to sulphate aerosol reduction, but this effect levels off after 2040. In the absence of climate change policies and with weak air pollution control (SSP3-7.0), the ''likely'' near-term warming due to changes in SLCFs is predominantly due to increases in methane, ozone and HFCs, with smaller contributions from changes in aerosols. SSP5-8.5 has the highest SLCF-induced warming rates due to warming from methane and ozone increases and reduced aerosols due to stronger air pollution control compared to the SSP3-7.0 scenario. {6.7.2}		'''In the next two decades, it is ''very likely'' that the SLCF emissions changes in the WGI core set of SSPs will cause a warming relative to 2019, whatever the SSPs, in addition to the warming from long-lived greenhouse gases. The net effect of SLCF and hydrofluorocarbon (HFC) changes on GSAT across the SSPs is a ''likely'' warming of 0.06°C–0.35°C in 2040 relative to 2019. Warming over the next two decades is quite similar across the SSPs due to competing effects of warming (methane, ozone) and cooling (aerosols) SLCFs.''' For the scenarios with the most stringent climate and air pollution mitigations (SSP1-1.9 and SSP1-2.6), the ''likely'' near-term warming from the SLCFs is predominantly due to sulphate aerosol reduction, but this effect levels off after 2040. In the absence of climate change policies and with weak air pollution control (SSP3-7.0), the ''likely'' near-term warming due to changes in SLCFs is predominantly due to increases in methane, ozone and HFCs, with smaller contributions from changes in aerosols. SSP5-8.5 has the highest SLCF-induced warming rates due to warming from methane and ozone increases and reduced aerosols due to stronger air pollution control compared to the SSP3-7.0 scenario. {6.7.2}

	'''At the end of the century, the large diversity of GSAT response to SLCF changes among the scenarios robustly covers the possible futures, as the scenarios are internally consistent and span a range from very high to very low emissions.''' In the scenarios without climate change mitigation (SSP3-7.0 and SSP5-8.5) the ''likely'' range of the estimated warming due to SLCFs in 2100 relative to 2019 is 0.4°C–0.9°C {6.7.3, 6.7.4} . In SSP3-7.0 there is a near-linear warming due to SLCFs of 0.08°C per decade, while for SSP5-8.5 there is a more rapid warming in the first half of the century. For the scenarios considering the most stringent climate and air pollution mitigations (SSP1-1.9 and SSP1-2.6), the reduced warming from reductions in methane, ozone and HFCs partly balances the warming from reduced aerosols, and the overall SLCF effect is a ''likely'' increase in GSAT of 0.0°C–0.3°C in 2100, relative to 2019. The SSP2-4.5 scenario (with moderate climate change and air pollution mitigations) results in a ''likely'' warming of 0.2°C–0.5°C in 2100 due to SLCFs, with the largest warming from reductions in aerosols. {6.7.3}		'''At the end of the century, the large diversity of GSAT response to SLCF changes among the scenarios robustly covers the possible futures, as the scenarios are internally consistent and span a range from very high to very low emissions.''' In the scenarios without climate change mitigation (SSP3-7.0 and SSP5-8.5) the ''likely'' range of the estimated warming due to SLCFs in 2100 relative to 2019 is 0.4°C–0.9°C {6.7.3, 6.7.4} . In SSP3-7.0 there is a near-linear warming due to SLCFs of 0.08°C per decade, while for SSP5-8.5 there is a more rapid warming in the first half of the century. For the scenarios considering the most stringent climate and air pollution mitigations (SSP1-1.9 and SSP1-2.6), the reduced warming from reductions in methane, ozone and HFCs partly balances the warming from reduced aerosols, and the overall SLCF effect is a ''likely'' increase in GSAT of 0.0°C–0.3°C in 2100, relative to 2019. The SSP2-4.5 scenario (with moderate climate change and air pollution mitigations) results in a ''likely'' warming of 0.2°C–0.5°C in 2100 due to SLCFs, with the largest warming from reductions in aerosols. {6.7.3}
Line 110:		Line 110:

	<span id="potential-effects-of-slcf-mitigation"></span>		<span id="potential-effects-of-slcf-mitigation"></span>

	=== Potential Effects of SLCF Mitigation ===		=== Potential Effects of SLCF Mitigation ===

Laura: /* Future Projections for Air Quality Considering Shared Socio-economic Pathways (SSPs) */

2026-06-24T09:45:43Z

Future Projections for Air Quality Considering Shared Socio-economic Pathways (SSPs)

← Older revision		Revision as of 09:45, 24 June 2026
Line 91:		Line 91:
	<div id="h2-3-siblings" class="h2-siblings"></div>		<div id="h2-3-siblings" class="h2-siblings"></div>

	'''Future air quality (in term of surface ozone and PM concentrations) on global to local scales will be primarily driven by changes in precursor emissions as opposed to climate change~~'''~~ (''high confidence~~'' '~~'') and climate change is projected to have mixed effects.''' A warmer climate is expected to reduce surface ozone in regions remote from pollution sources (''high confidence'') but is expected to increase it by a few parts per billion over polluted regions, depending on ozone precursor levels (''medium'' to ''high confidence''). Future climate change is expected to have mixed effects, positive or negative, with an overall low effect, on global surface PM and more generally on the aerosol global burden (''medium confidence'') '','' but stronger effects are not excluded in regions prone to specific meteorological conditions (''low confidence''). Overall, there is ''low confidence'' in the response of surface ozone and PM to future climate change due to the uncertainty in the response of the natural processes (e.g., stratosphere–troposphere exchange, natural precursor emissions, particularly including biogenic volatile organic compounds, wildfire-emitted precursors, land and marine aerosols, and lightning NO <sub>x</sub>) to climate change. {6.3, 6.5}		'''Future air quality (in term of surface ozone and PM concentrations) on global to local scales will be primarily driven by changes in precursor emissions as opposed to climate change (''high confidence'') and climate change is projected to have mixed effects.''' A warmer climate is expected to reduce surface ozone in regions remote from pollution sources (''high confidence'') but is expected to increase it by a few parts per billion over polluted regions, depending on ozone precursor levels (''medium'' to ''high confidence''). Future climate change is expected to have mixed effects, positive or negative, with an overall low effect, on global surface PM and more generally on the aerosol global burden (''medium confidence'') '','' but stronger effects are not excluded in regions prone to specific meteorological conditions (''low confidence''). Overall, there is ''low confidence'' in the response of surface ozone and PM to future climate change due to the uncertainty in the response of the natural processes (e.g., stratosphere–troposphere exchange, natural precursor emissions, particularly including biogenic volatile organic compounds, wildfire-emitted precursors, land and marine aerosols, and lightning NO <sub>x</sub>) to climate change. {6.3, 6.5}

	'''The SSPs span a wider range of SLCF emissions than the Representative Concentration Pathways (RCPs), representing better the diversity of future options in air pollution management~~'''~~ (''high confidence~~'' '~~'').''' In the SSPs, the socio-economic assumptions and climate change mitigation levels primarily drive future emissions, but the SLCF emissions trajectories are also steered by varying levels of air pollution control originating from the SSP narratives, independently from climate change mitigation. Consequently, SSPs consider a large variety of regional ambitions and effectiveness in implementing air pollution legislation and result in wider range of future air pollution levels and SLCF-induced climate effects. {6.7.1}		'''The SSPs span a wider range of SLCF emissions than the Representative Concentration Pathways (RCPs), representing better the diversity of future options in air pollution management (''high confidence'').''' In the SSPs, the socio-economic assumptions and climate change mitigation levels primarily drive future emissions, but the SLCF emissions trajectories are also steered by varying levels of air pollution control originating from the SSP narratives, independently from climate change mitigation. Consequently, SSPs consider a large variety of regional ambitions and effectiveness in implementing air pollution legislation and result in wider range of future air pollution levels and SLCF-induced climate effects. {6.7.1}

	'''Air pollution projections range from strong reductions in global surface ozone and PM (e.g.,~~''' '''~~SSP1-2.6~~''' '''~~, with strong mitigation of both air pollution and climate change) to no improvement and even degradation (e.g.,~~''' '''~~SSP3-7.0~~''' '''~~without climate change mitigation and with only weak air pollution control)~~'''~~ (''high confidence~~'' '~~'').''' Under the SSP3-7.0 scenario, PM levels are projected to increase until 2050 over large parts of Asia, and surface ozone pollution is projected to worsen over all continental areas through 2100 (''high confidence''). Without climate change mitigation but with stringent air pollution control (SSP5-8.5), PM levels decline through 2100, but high methane levels hamper the decline in global surface ozone at least until 2080 (''high confidence''). {6.7.1}		'''Air pollution projections range from strong reductions in global surface ozone and PM (e.g., SSP1-2.6, with strong mitigation of both air pollution and climate change) to no improvement and even degradation (e.g., SSP3-7.0 without climate change mitigation and with only weak air pollution control) (''high confidence'').''' Under the SSP3-7.0 scenario, PM levels are projected to increase until 2050 over large parts of Asia, and surface ozone pollution is projected to worsen over all continental areas through 2100 (''high confidence''). Without climate change mitigation but with stringent air pollution control (SSP5-8.5), PM levels decline through 2100, but high methane levels hamper the decline in global surface ozone at least until 2080 (''high confidence''). {6.7.1}

	<span id="future-projections-of-the-effect-of-slcfs-on-gsat-in-the-core-ssps"></span>		<span id="future-projections-of-the-effect-of-slcfs-on-gsat-in-the-core-ssps"></span>

	=== Future Projections of the Effect of SLCFs on GSAT in the Core SSPs ===		=== Future Projections of the Effect of SLCFs on GSAT in the Core SSPs ===

Laura: /* Effect of SLCFs on Climate and Biogeochemical Cycles */

2026-06-24T09:44:55Z

Effect of SLCFs on Climate and Biogeochemical Cycles

← Older revision		Revision as of 09:44, 24 June 2026
Line 71:		Line 71:
	<div id="h2-2-siblings" class="h2-siblings"></div>		<div id="h2-2-siblings" class="h2-siblings"></div>

	'''Over the historical period, changes in aerosols and their effective radiative forcing (ERF) have primarily contributed to a surface cooling, partly masking the greenhouse gas-driven warming~~'''~~ (''high confidence~~'' '~~'').''' Radiative forcings induced by aerosol changes lead to both local and remote temperature responses (''high confidence''). The temperature response preserves the south to north gradient of the aerosol ERF – hemispherical asymmetry – but is more uniform with latitude and is strongly amplified towards the Arctic (''medium confidence''). {6.4.1, 6.4.3}		'''Over the historical period, changes in aerosols and their effective radiative forcing (ERF) have primarily contributed to a surface cooling, partly masking the greenhouse gas-driven warming (''high confidence'').''' Radiative forcings induced by aerosol changes lead to both local and remote temperature responses (''high confidence''). The temperature response preserves the south to north gradient of the aerosol ERF – hemispherical asymmetry – but is more uniform with latitude and is strongly amplified towards the Arctic (''medium confidence''). {6.4.1, 6.4.3}

	'''Since the mid-1970s, trends in aerosols and their precursor emissions have led to a shift from an increase to a decrease of the magnitude of the negative globally averaged net aerosol ERF~~'''~~ (''high confidence~~'' '~~'').''' However, the timing of this shift varies by continental-scale region and has not occured for some finer regional scales. The spatial and temporal distribution of the net aerosol ERF from 1850 to 2014 is highly heterogeneous, with stronger magnitudes in the Northern Hemisphere (''high confidence''). {6.4.1}		'''Since the mid-1970s, trends in aerosols and their precursor emissions have led to a shift from an increase to a decrease of the magnitude of the negative globally averaged net aerosol ERF (''high confidence'').''' However, the timing of this shift varies by continental-scale region and has not occured for some finer regional scales. The spatial and temporal distribution of the net aerosol ERF from 1850 to 2014 is highly heterogeneous, with stronger magnitudes in the Northern Hemisphere (''high confidence''). {6.4.1}

	'''For forcers with short lifetimes (e.g., months) and not considering chemical adjustments, the response in surface temperature occurs strongly as soon as a sustained change in emissions is implemented, and that response continues to grow for a few years, primarily due to thermal inertia in the climate system~~'''~~ (''high confidence~~'' '~~'')''' . Near its maximum, the response slows down but will then take centuries to reach equilibrium (''high confidence''). For SLCFs with longer lifetimes (e.g., a decade), a delay equivalent to their lifetimes is appended to the delay due to thermal inertia (''high confidence''). {6.6.1}		'''For forcers with short lifetimes (e.g., months) and not considering chemical adjustments, the response in surface temperature occurs strongly as soon as a sustained change in emissions is implemented, and that response continues to grow for a few years, primarily due to thermal inertia in the climate system (''high confidence'')''' . Near its maximum, the response slows down but will then take centuries to reach equilibrium (''high confidence''). For SLCFs with longer lifetimes (e.g., a decade), a delay equivalent to their lifetimes is appended to the delay due to thermal inertia (''high confidence''). {6.6.1}

	'''Over the 1750–2019 period, changes in SLCF emissions, especially of methane, NO~~'''~~ <sub>x</sub> ~~'''~~and SO~~'''~~ <sub>2</sub> , ~~'''~~have substantial effects on effective radiative forcing (ERF)~~'''~~ (''high confidence~~'' '~~'').''' The net global emissions-based ERF of NO <sub>x</sub> is negative and that of non-methane volatile organic compounds (NMVOCs) is positive, in agreement with the AR5 Assessment (''high confidence''). For methane, the emissions-based ERF is twice as high as the abundance-based ERF (''high confidence'') attributed to chemical adjustment mainly via ozone production. SO <sub>2</sub> emissions changes make the dominant contribution to the ERF from aerosol–cloud interactions (''high'' ''confidence''). Over the 1750–2019 period, the contributions from the emitted compounds to changes in global surface air temperature (GSAT) broadly match their contributions to the ERF (''high confidence''). Since a peak in emissions-induced SO <sub>2</sub> eRF has already occurred recently and since there is a delay in the full GSAT response, changes in SO <sub>2</sub> emissions have a slightly larger contribution to GSAT change than CO <sub>2</sub> emissions, relative to their respective contributions to ERF. {6.4.2, 6.6.1 and 7.3.5}		'''Over the 1750–2019 period, changes in SLCF emissions, especially of methane, NO<sub>x</sub> and SO<sub>2</sub>, have substantial effects on effective radiative forcing (ERF) (''high confidence'').''' The net global emissions-based ERF of NO <sub>x</sub> is negative and that of non-methane volatile organic compounds (NMVOCs) is positive, in agreement with the AR5 Assessment (''high confidence''). For methane, the emissions-based ERF is twice as high as the abundance-based ERF (''high confidence'') attributed to chemical adjustment mainly via ozone production. SO <sub>2</sub> emissions changes make the dominant contribution to the ERF from aerosol–cloud interactions (''high'' ''confidence''). Over the 1750–2019 period, the contributions from the emitted compounds to changes in global surface air temperature (GSAT) broadly match their contributions to the ERF (''high confidence''). Since a peak in emissions-induced SO <sub>2</sub> eRF has already occurred recently and since there is a delay in the full GSAT response, changes in SO <sub>2</sub> emissions have a slightly larger contribution to GSAT change than CO <sub>2</sub> emissions, relative to their respective contributions to ERF. {6.4.2, 6.6.1 and 7.3.5}

	'''Reactive nitrogen, ozone and aerosols affect terrestrial vegetation and the carbon cycle through deposition and effects on large-scale radiation~~'''~~ (''high confidence~~'' '~~'').''' However, the magnitude of these effects on the land carbon sink, ecosystem productivity and hence their indirect CO <sub>2</sub> forcing remain uncertain due to the difficulty in disentangling the complex interactions between the individual effects. As such, these effects are assessed to be of second order in comparison to the direct CO <sub>2</sub> forcing (''high confidence''), but effects of ozone on terrestrial vegetation could add a substantial (positive) forcing compared with the direct ozone forcing (''low confidence''). {6.4.4}		'''Reactive nitrogen, ozone and aerosols affect terrestrial vegetation and the carbon cycle through deposition and effects on large-scale radiation (''high confidence'').''' However, the magnitude of these effects on the land carbon sink, ecosystem productivity and hence their indirect CO <sub>2</sub> forcing remain uncertain due to the difficulty in disentangling the complex interactions between the individual effects. As such, these effects are assessed to be of second order in comparison to the direct CO <sub>2</sub> forcing (''high confidence''), but effects of ozone on terrestrial vegetation could add a substantial (positive) forcing compared with the direct ozone forcing (''low confidence''). {6.4.4}

	'''Climate feedbacks induced from changes in emissions, abundances or lifetimes of SLCFs mediated by natural processes or atmospheric chemistry are assessed to have an overall cooling effect~~'''~~ (''low confidence~~'' '~~''), that is, a total negative feedback parameter of –0.20 [–0.41 to +0.01] W m''' <sup>–2</sup> ~~'''~~°C~~'''~~ <sup>−1</sup> ~~'''~~.''' These non-CO <sub>2</sub> biogeochemical feedbacks are estimated from ESMs, which have advanced since AR5 to include a consistent representation of biogeochemical cycles and atmospheric chemistry. However, process-level understanding of many chemical and biogeochemical feedbacks involving SLCFs, particularly natural emissions, is still emerging, resulting in ''low confidence'' in the magnitude and sign of most SLCF climate feedback parameters. {6.2.2, 6.4.5}		'''Climate feedbacks induced from changes in emissions, abundances or lifetimes of SLCFs mediated by natural processes or atmospheric chemistry are assessed to have an overall cooling effect (''low confidence''), that is, a total negative feedback parameter of –0.20 [–0.41 to +0.01] W m'<sup>–2</sup> °C <sup>−1</sup>.''' These non-CO <sub>2</sub> biogeochemical feedbacks are estimated from ESMs, which have advanced since AR5 to include a consistent representation of biogeochemical cycles and atmospheric chemistry. However, process-level understanding of many chemical and biogeochemical feedbacks involving SLCFs, particularly natural emissions, is still emerging, resulting in ''low confidence'' in the magnitude and sign of most SLCF climate feedback parameters. {6.2.2, 6.4.5}

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

	<span id="future-projections-for-air-quality-considering-shared-socio-economic-pathways-ssps"></span>		<span id="future-projections-for-air-quality-considering-shared-socio-economic-pathways-ssps"></span>

	=== Future Projections for Air Quality Considering Shared Socio-economic Pathways (SSPs) ===		=== Future Projections for Air Quality Considering Shared Socio-economic Pathways (SSPs) ===

Laura: /* Recent Evolution in Short-lived Climate Forcer (SLCF) Emissions and Abundances */

2026-06-24T09:43:44Z

Recent Evolution in Short-lived Climate Forcer (SLCF) Emissions and Abundances

← Older revision		Revision as of 09:43, 24 June 2026
Line 59:		Line 59:
	<div id="h2-1-siblings" class="h2-siblings"></div>		<div id="h2-1-siblings" class="h2-siblings"></div>

	'''Over the last decade (2010–2019), strong shifts in the geographical distribution of emissions have led to changes in atmospheric abundances of highly variable SLCFs~~'''~~ (''high confidence~~'' '~~''). Evidence from satellite and surface observations shows strong regional variations in trends of ozone (O~~'''~~ <sub>3</sub> ~~'''~~), aerosols and their precursors~~'''~~ (''high confidence~~'' '~~'').''' In particular, tropospheric columns of nitrogen dioxide (NO <sub>2</sub>) and sulphur dioxide (SO <sub>2</sub>) continued to decline over North America and Europe (''high confidence''), and to increase over Southern Asia (''medium confidence''), but have declined over Eastern Asia (''high confidence''). Global carbon monoxide (CO) abundance has continued to decline (''high confidence''). The concentrations of hydrofluorocarbons (HFCs) are increasing (''high confidence''). Global carbonaceous aerosol budgets and trends remain poorly characterized due to limited observations, but sites representative of background conditions have reported multi-year declines in black carbon (BC) over several regions of the Northern Hemisphere. {6.2, 6.3, 2.2.4, 2.2.5, 2.2.6}		'''Over the last decade (2010–2019), strong shifts in the geographical distribution of emissions have led to changes in atmospheric abundances of highly variable SLCFs (''high confidence''). Evidence from satellite and surface observations shows strong regional variations in trends of ozone (O<sub>3</sub>), aerosols and their precursors (''high confidence'').''' In particular, tropospheric columns of nitrogen dioxide (NO <sub>2</sub>) and sulphur dioxide (SO <sub>2</sub>) continued to decline over North America and Europe (''high confidence''), and to increase over Southern Asia (''medium confidence''), but have declined over Eastern Asia (''high confidence''). Global carbon monoxide (CO) abundance has continued to decline (''high confidence''). The concentrations of hydrofluorocarbons (HFCs) are increasing (''high confidence''). Global carbonaceous aerosol budgets and trends remain poorly characterized due to limited observations, but sites representative of background conditions have reported multi-year declines in black carbon (BC) over several regions of the Northern Hemisphere. {6.2, 6.3, 2.2.4, 2.2.5, 2.2.6}

	'''There is no significant trend in the global mean tropospheric concentration of hydroxyl (OH) radical – the main sink for many SLCFs, including methane (CH~~'''~~ <sub>4</sub> ~~'''~~) – from 1850 up to around 1980~~'''~~ (''low confidence~~'' '~~'') but OH has remained stable or exhibited a positive trend since the 1980s~~'''~~ (''medium confidence~~'' '~~'').''' Global OH cannot be measured directly and is inferred from Earth system and chemistry–climate models (ESMs, CCMs) constrained by emissions and from observationally constrained inversion methods. There is conflicting information from these methods for the 1980–2014 period. ESMs and CCMs concur on a positive trend since 1980 (about a 9% increase over 1980–2014) and there is ''medium confidence'' that this trend is mainly driven by increases in global anthropogenic (human-caused) nitrogen oxide (NO <sub>x</sub>) emissions and decreases in anthropogenic CO emissions. The observation-constrained methods suggest either positive trends or the absence of trends based on ''limited evidence'' and ''medium agreement.'' Future changes in global OH, in response to SLCF emissions and climate change, will depend on the interplay between multiple offsetting drivers of OH. {6.3.6 and Cross-Chapter Box 5.1}		'''There is no significant trend in the global mean tropospheric concentration of hydroxyl (OH) radical – the main sink for many SLCFs, including methane (CH<sub>4</sub>) – from 1850 up to around 1980 (''low confidence'') but OH has remained stable or exhibited a positive trend since the 1980s (''medium confidence'').''' Global OH cannot be measured directly and is inferred from Earth system and chemistry–climate models (ESMs, CCMs) constrained by emissions and from observationally constrained inversion methods. There is conflicting information from these methods for the 1980–2014 period. ESMs and CCMs concur on a positive trend since 1980 (about a 9% increase over 1980–2014) and there is ''medium confidence'' that this trend is mainly driven by increases in global anthropogenic (human-caused) nitrogen oxide (NO <sub>x</sub>) emissions and decreases in anthropogenic CO emissions. The observation-constrained methods suggest either positive trends or the absence of trends based on ''limited evidence'' and ''medium agreement.'' Future changes in global OH, in response to SLCF emissions and climate change, will depend on the interplay between multiple offsetting drivers of OH. {6.3.6 and Cross-Chapter Box 5.1}

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

	<span id="effect-of-slcfs-on-climate-and-biogeochemical-cycles"></span>		<span id="effect-of-slcfs-on-climate-and-biogeochemical-cycles"></span>

	=== Effect of SLCFs on Climate and Biogeochemical Cycles ===		=== Effect of SLCFs on Climate and Biogeochemical Cycles ===

Laura at 09:42, 24 June 2026

2026-06-24T09:42:45Z

Show changes

Laura at 08:35, 22 May 2026

2026-05-22T08:35:25Z

← Older revision		Revision as of 08:35, 22 May 2026
Line 1:		Line 1:
			{{ChapterNavigation
			\| cover = Cover-WGI.jpg
			\| reporturl = IPCC:AR6/WGI
			\| reporttitle = WGI
			\| prevurl = IPCC:AR6/WGI/Chapter-5
			\| nexturl = IPCC:AR6/WGI/Chapter-7
			}}

	<div id="___gatsby"></div>		<div id="___gatsby"></div>