IPCC:AR6/WGI/Chapter-1 - Revision history

Laura: /* Appendix 1.A. Historical Overview of Major Conclusions of IPCC Assessment Reports */

2026-06-24T09:56:45Z

Appendix 1.A. Historical Overview of Major Conclusions of IPCC Assessment Reports

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	\|}		\|}

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

	<div id="footnote-007" class="_idFootnote"></div>		<div id="footnote-007" class="_idFootnote"></div>

Laura: /* 1.6.3 Cumulative Carbon Dioxide Emissions */

2026-06-22T11:05:26Z

1.6.3 Cumulative Carbon Dioxide Emissions

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			<div class="container-box col-regular">
	~~'''~~Box 1.4 \| The Relationships Between 'Net Zero' Emissions, Temperature Outcomes and Carbon Dioxide Removal~~'''~~		== Box 1.4 \| The Relationships Between 'Net Zero' Emissions, Temperature Outcomes and Carbon Dioxide Removal ==

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	CDR can be achieved through a number of measures (Section 5.6; SRCCL , [[#IPCC--2019a\|IPCC, 2019a]]) . These include additional afforestation, reforestation, soil carbon management, biochar, direct air capture and carbon capture and storage (DACCS), and bioenergy with carbon capture and storage (BECCS; [[#de%20Coninck--2018\|de Coninck et al., 2018]], SR1.5 Ch4; [[#Minx--2018\|Minx et al., 2018]]; see also WGIII Chapters 7 and 12). Differences between land use, land-use change and forestry (LULUCF) accounting rules, and scientific bookkeeping approaches for CO <sub>2</sub> emissions and removals from the terrestrial biosphere, can result in significant differences between the amount of CDR that is reported in different studies ([[#Grassi--2017\|Grassi et al., 2017]]). Different measures to achieve CDR come with different risks, negative side effects and potential co-benefits – also in conjunction with sustainable development goals – that can inform choices around their implementation (Section 5.6; [[#Fuss--2018\|Fuss et al., 2018]]; [[#Roe--2019\|Roe et al., 2019]]). Technologies to achieve direct large-scale anthropogenic removals of non-CO <sub>2</sub> GHGs are speculative at present (Yoon et al. , 2009; Ming et al. , 2016; Kroeger et al. , 2017; Jackson et al., 2019) .		CDR can be achieved through a number of measures (Section 5.6; SRCCL , [[#IPCC--2019a\|IPCC, 2019a]]) . These include additional afforestation, reforestation, soil carbon management, biochar, direct air capture and carbon capture and storage (DACCS), and bioenergy with carbon capture and storage (BECCS; [[#de%20Coninck--2018\|de Coninck et al., 2018]], SR1.5 Ch4; [[#Minx--2018\|Minx et al., 2018]]; see also WGIII Chapters 7 and 12). Differences between land use, land-use change and forestry (LULUCF) accounting rules, and scientific bookkeeping approaches for CO <sub>2</sub> emissions and removals from the terrestrial biosphere, can result in significant differences between the amount of CDR that is reported in different studies ([[#Grassi--2017\|Grassi et al., 2017]]). Different measures to achieve CDR come with different risks, negative side effects and potential co-benefits – also in conjunction with sustainable development goals – that can inform choices around their implementation (Section 5.6; [[#Fuss--2018\|Fuss et al., 2018]]; [[#Roe--2019\|Roe et al., 2019]]). Technologies to achieve direct large-scale anthropogenic removals of non-CO <sub>2</sub> GHGs are speculative at present (Yoon et al. , 2009; Ming et al. , 2016; Kroeger et al. , 2017; Jackson et al., 2019) .
			</div>
	<div id="1.7" class="h1-container"></div>		<div id="1.7" class="h1-container"></div>

Laura: /* Cross-Chapter Box 1.4 | The SSP Scenarios as Used in Working Group I (WGI) */

2026-06-22T11:04:30Z

Cross-Chapter Box 1.4 | The SSP Scenarios as Used in Working Group I (WGI)

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	<sup>a</sup> The AerChemMIP variant of SSP3-7.0 -lowNTCF (Collins et al. , 2017) only reduced aerosol and ozone precursors compared to SSP3-7.0 , not methane. The SSP3-7.0-lowNTCF variant by the integrated assessment models also reduced methane emissions (Gidden et al. , 2019), which creates differences between SSP3-7.0-lowNTCF and SSP3-7.0 also in terms of methane concentrations and some fluorinated gas concentrations that have OH related sinks (Meinshausen et al., 2020).		<sup>a</sup> The AerChemMIP variant of SSP3-7.0 -lowNTCF (Collins et al. , 2017) only reduced aerosol and ozone precursors compared to SSP3-7.0 , not methane. The SSP3-7.0-lowNTCF variant by the integrated assessment models also reduced methane emissions (Gidden et al. , 2019), which creates differences between SSP3-7.0-lowNTCF and SSP3-7.0 also in terms of methane concentrations and some fluorinated gas concentrations that have OH related sinks (Meinshausen et al., 2020).

	'''Cross-Chapter Box 1.4, Table 2 \|'''		'''Cross-Chapter Box 1.4, Table 2 \|''' '''Overview of key climate forcer datasets used as input by ESMs for historical and future SSP scenario experiments.''' The data is available from the Earth System Grid Federation ([[#ESGF--2021\|ESGF, 2021]]) described in [[#Eyring--2016\|Eyring et al. (2016)]].

	'''Overview of key climate forcer datasets used as input by ESMs for historical and future SSP scenario experiments.''' The data is available from the Earth System Grid Federation ([[#ESGF--2021\|ESGF, 2021]]) described in [[#Eyring--2016\|Eyring et al. (2016)]].
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	<span id="final-remarks"></span>		<span id="final-remarks"></span>

	== 1.7 Final Remarks ==		== 1.7 Final Remarks ==

Laura: /* 1.6.1.4 The Likelihood of Reference Scenarios, Scenario Uncertainty and Storylines */

2026-06-22T11:03:30Z

1.6.1.4 The Likelihood of Reference Scenarios, Scenario Uncertainty and Storylines

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	~~'''~~Cross-Chapter Box 1.4 \| The SSP Scenarios as Used in ~~Workin''' '''g~~ Group I (WGI)~~'''~~		== Cross-Chapter Box 1.4 \| The SSP Scenarios as Used in Working Group I (WGI) ==

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	[[File:24bb5b5d69b887eac6b5ee61645ca628 IPCC_AR6_WGI_CCBox_1_4_Figure_1.png]]		[[File:24bb5b5d69b887eac6b5ee61645ca628 IPCC_AR6_WGI_CCBox_1_4_Figure_1.png]]

	'''Cross-Chapter Box 1.4, Figure 1 \|'''		'''Cross-Chapter Box 1.4, Figure 1 \|''' '''The SSP scenarios used in this Report, their indicative temperature evolution and radiative forcing categorization, and the five socio-economic storylines upon which they are built.''' The core set of scenarios used in this report – i.e., SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 – is shown together with an additional four SSPs that are part of ScenarioMIP, as well as previous RCP scenarios. In the '''left-hand panel''' , the indicative temperature evolution is shown (adapted from Meinshausen et al. , 2020) . The black stripes on the respective scenario family panels on the left-hand side indicate a larger set of IAM-based SSP scenarios that span the scenario range more fully, but are not used in this report. The SSP–radiative forcing matrix is shown on the '''right-hand panel''' , with the SSP socio-economic narratives shown as columns and the indicative radiative forcing categorization by 2100 shown as rows. Note that the descriptive labels for the five SSP narratives refer mainly to the reference scenario futures without additional climate policies. For example, SSP5 can accommodate strong mitigation scenarios leading to net zero emissions; these do not match a ‘fossil-fuelled development’ label. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

	'''The SSP scenarios used in this Report, their indicative temperature evolution and radiative forcing categorization, and the five socio-economic storylines upon which they are built.''' The core set of scenarios used in this report – i.e., SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 – is shown together with an additional four SSPs that are part of ScenarioMIP, as well as previous RCP scenarios. In the '''left-hand panel''' , the indicative temperature evolution is shown (adapted from Meinshausen et al. , 2020) . The black stripes on the respective scenario family panels on the left-hand side indicate a larger set of IAM-based SSP scenarios that span the scenario range more fully, but are not used in this report. The SSP–radiative forcing matrix is shown on the '''right-hand panel''' , with the SSP socio-economic narratives shown as columns and the indicative radiative forcing categorization by 2100 shown as rows. Note that the descriptive labels for the five SSP narratives refer mainly to the reference scenario futures without additional climate policies. For example, SSP5 can accommodate strong mitigation scenarios leading to net zero emissions; these do not match a ‘fossil-fuelled development’ label. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).		'''Cross-Chapter Box 1.4, Table 1 \|''' '''Overview of SSP scenarios used in this report.''' The middle column briefly describes the SSP scenarios and the right-hand column indicates the previous RCP scenarios that most closely match that SSP’s assessed global surface air temperature (GSAT) trajectory. RCP scenarios are generally found to result in larger modelled warming for the same nominal radiative forcing label ([[IPCC:Wg1:Chapter:Chapter-4#4.6.2.2\|Section 4.6.2.2]]). The five core SSP scenarios used most commonly in this report are highlighted in bold . Further SSP scenarios are used where they allow assessment of specific aspects, e.g., air pollution policies in [[IPCC:Wg1:Chapter:Chapter-6\|Chapter 6]] (SSP3-7.0-lowNTCF). RCPs are used in this report wherever the relevant scientific literature makes substantial use of regional or domain-specific model output that is based on these previous RCP pathways, such as sea level rise projections in [[IPCC:Wg1:Chapter:Chapter-9\|Chapter 9]] (Section 9.6.3.1) or regional climate aspects in Chapters 10 and 12. See ([[IPCC:Wg1:Chapter:Chapter-4\|Chapter 4]] ([[IPCC:Wg1:Chapter:Chapter-4#4.3.4\|Section 4.3.4]]) for the GSAT assessment for the SSP scenarios and [[IPCC:Wg1:Chapter:Chapter-4#4.6.2.2\|Section 4.6.2.2]] for a comparison between SSPs and RCPs in terms of both radiative forcing and global surface temperature.

	'''Cross-Chapter Box 1.4, Table 1 \|'''

	'''Overview of SSP scenarios used in this report.''' The middle column briefly describes the SSP scenarios and the right-hand column indicates the previous RCP scenarios that most closely match that SSP’s assessed global surface air temperature (GSAT) trajectory. RCP scenarios are generally found to result in larger modelled warming for the same nominal radiative forcing label ([[IPCC:Wg1:Chapter:Chapter-4#4.6.2.2\|Section 4.6.2.2]]). The five core SSP scenarios used most commonly in this report are highlighted in bold . Further SSP scenarios are used where they allow assessment of specific aspects, e.g., air pollution policies in [[IPCC:Wg1:Chapter:Chapter-6\|Chapter 6]] (SSP3-7.0-lowNTCF). RCPs are used in this report wherever the relevant scientific literature makes substantial use of regional or domain-specific model output that is based on these previous RCP pathways, such as sea level rise projections in [[IPCC:Wg1:Chapter:Chapter-9\|Chapter 9]] (Section 9.6.3.1) or regional climate aspects in Chapters 10 and 12. See ([[IPCC:Wg1:Chapter:Chapter-4\|Chapter 4]] ([[IPCC:Wg1:Chapter:Chapter-4#4.3.4\|Section 4.3.4]]) for the GSAT assessment for the SSP scenarios and [[IPCC:Wg1:Chapter:Chapter-4#4.6.2.2\|Section 4.6.2.2]] for a comparison between SSPs and RCPs in terms of both radiative forcing and global surface temperature.
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	'''Cross-Chapter Box 1.4, Figure 2:''' Also shown are gridded emissions differences for SO <sub>2</sub> '''(p)''' and black carbon '''(q)''' for the year 2000 between the input emissions datasets that underpinned the CMIP5 and CMIP6 model intercomparisons. Historical emissions estimates are provided in black in panels '''(d–o)''' . The range of concentrations and emissions investigated under the RCP pathways is shaded grey. Panels (p) and (q) adapted from Figure 7 in [[#Hoesly--2018\|Hoesly et al. (2018)]]. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).		'''Cross-Chapter Box 1.4, Figure 2:''' Also shown are gridded emissions differences for SO <sub>2</sub> '''(p)''' and black carbon '''(q)''' for the year 2000 between the input emissions datasets that underpinned the CMIP5 and CMIP6 model intercomparisons. Historical emissions estimates are provided in black in panels '''(d–o)''' . The range of concentrations and emissions investigated under the RCP pathways is shaded grey. Panels (p) and (q) adapted from Figure 7 in [[#Hoesly--2018\|Hoesly et al. (2018)]]. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).
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	<div id="1.6.2" class="h2-container"></div>		<div id="1.6.2" class="h2-container"></div>

	<span id="global-warming-levels"></span>		<span id="global-warming-levels"></span>

	=== 1.6.2 Global Warming Levels ===		=== 1.6.2 Global Warming Levels ===

Laura: /* 1.6.1.1 Shared Socio-economic Pathways */

2026-06-22T11:02:09Z

1.6.1.1 Shared Socio-economic Pathways

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	[[File:80356ae2da8a03e6aeb0d297efbb29ab IPCC_AR6_WGI_Chapter_1_Table_1_3.png]]		[[File:80356ae2da8a03e6aeb0d297efbb29ab IPCC_AR6_WGI_Chapter_1_Table_1_3.png]]

	This SSP scenario categorization, focused on end-of-century radiative forcing levels, reflects how scenarios were conceptualized until recently, namely, to reach a particular climate target in 2100 at the lowest cost and irrespective of whether the target was exceeded over the century. More recently, and in particular since IPCC SR1.5 report focused attention on peak warming scenarios ([[#Rogelj--2018b\|Rogelj et al., 2018b]]), scenario development started to explicitly consider peak warming, cumulative emissions and the amount of net negative emissions ([[#Rogelj--2018b\|Rogelj et al., 2018b]]; [[#Fujimori--2019\|Fujimori et al., 2019]]).		This SSP scenario categorization, focused on end-of-century radiative forcing levels, reflects how scenarios were conceptualized until recently, namely, to reach a particular climate target in 2100 at the lowest cost and irrespective of whether the target was exceeded over the century. More recently, and in particular since IPCC SR1.5 report focused attention on peak warming scenarios ([[#Rogelj--2018b\|Rogelj et al., 2018b]]), scenario development started to explicitly consider peak warming, cumulative emissions and the amount of net negative emissions ([[#Rogelj--2018b\|Rogelj et al., 2018b]]; [[#Fujimori--2019\|Fujimori et al., 2019]]).

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	<span id="scenario-generation-process-for-cmip6"></span>		<span id="scenario-generation-process-for-cmip6"></span>

	==== 1.6.1.2 Scenario Generation Process for CMIP6 ====		==== 1.6.1.2 Scenario Generation Process for CMIP6 ====

Laura: /* 1.5.4.3 The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) */

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1.5.4.3 The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6)

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	The CMIP DECK simulations form the basis for a range of assessments and projections in the following chapters. As in CMIP5, they consist of: a ‘pre-industrial’ control simulation (piControl, where ‘pre-industrial’ is taken as fixed 1850 conditions in these experiments); an idealized, abrupt quadrupling of CO <sub>2</sub> concentrations relative to piControl (to estimate equilibrium climate sensitivity); a 1% per year increase in CO <sub>2</sub> concentrations relative to piControl (to estimate the transient climate response); and a transient simulation with prescribed sea-surface temperatures for the period 1979–2014 (termed ‘AMIP’ for historical reasons). In addition, all participating models perform a historical simulation for the period 1850–2014. For the latter, common CMIP6 forcings are prescribed (Cross-Chapter Box 1.4, Table 2). Depending on the model setup, these include emissions and concentrations of short-lived species ([[#Hoesly--2018\|Hoesly et al., 2018]]; [[#Gidden--2019\|Gidden et al., 2019]]), long-lived GHGs ([[#Meinshausen--2017\|Meinshausen et al., 2017]]), biomass burning emissions ([[#van%20Marle--2017\|van Marle et al., 2017]]), global gridded land-use forcing data ([[#Ma--2020\|Ma et al., 2020]]), solar forcing ([[#Matthes--2017\|Matthes et al., 2017]]), and stratospheric aerosol data from volcanoes ([[#Zanchettin--2016\|Zanchettin et al., 2016]]). The methods for generating gridded datasets are described in [[#Feng--2020\|Feng et al. (2020)]]. For AMIP simulations, common sea surface temperatures (SSTs) and sea ice concentrations (SICs) are prescribed. For simulations with prescribed aerosol abundances (i.e., not calculated from emissions), optical properties and fractional changes in cloud droplet effective radius are generally prescribed in order to provide a more consistent representation of aerosol forcing relative to earlier CMIP phases ([[#Fiedler--2017\|Fiedler et al., 2017]]; [[#Stevens--2017\|Stevens et al., 2017]]). For models without ozone chemistry, time-varying gridded ozone concentrations and nitrogen deposition are also provided ([[#Checa-Garcia--2018\|Checa-Garcia et al., 2018]]).		The CMIP DECK simulations form the basis for a range of assessments and projections in the following chapters. As in CMIP5, they consist of: a ‘pre-industrial’ control simulation (piControl, where ‘pre-industrial’ is taken as fixed 1850 conditions in these experiments); an idealized, abrupt quadrupling of CO <sub>2</sub> concentrations relative to piControl (to estimate equilibrium climate sensitivity); a 1% per year increase in CO <sub>2</sub> concentrations relative to piControl (to estimate the transient climate response); and a transient simulation with prescribed sea-surface temperatures for the period 1979–2014 (termed ‘AMIP’ for historical reasons). In addition, all participating models perform a historical simulation for the period 1850–2014. For the latter, common CMIP6 forcings are prescribed (Cross-Chapter Box 1.4, Table 2). Depending on the model setup, these include emissions and concentrations of short-lived species ([[#Hoesly--2018\|Hoesly et al., 2018]]; [[#Gidden--2019\|Gidden et al., 2019]]), long-lived GHGs ([[#Meinshausen--2017\|Meinshausen et al., 2017]]), biomass burning emissions ([[#van%20Marle--2017\|van Marle et al., 2017]]), global gridded land-use forcing data ([[#Ma--2020\|Ma et al., 2020]]), solar forcing ([[#Matthes--2017\|Matthes et al., 2017]]), and stratospheric aerosol data from volcanoes ([[#Zanchettin--2016\|Zanchettin et al., 2016]]). The methods for generating gridded datasets are described in [[#Feng--2020\|Feng et al. (2020)]]. For AMIP simulations, common sea surface temperatures (SSTs) and sea ice concentrations (SICs) are prescribed. For simulations with prescribed aerosol abundances (i.e., not calculated from emissions), optical properties and fractional changes in cloud droplet effective radius are generally prescribed in order to provide a more consistent representation of aerosol forcing relative to earlier CMIP phases ([[#Fiedler--2017\|Fiedler et al., 2017]]; [[#Stevens--2017\|Stevens et al., 2017]]). For models without ozone chemistry, time-varying gridded ozone concentrations and nitrogen deposition are also provided ([[#Checa-Garcia--2018\|Checa-Garcia et al., 2018]]).

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	<span id="coordinated-regional-downscaling-experiment-cordex"></span>		<span id="coordinated-regional-downscaling-experiment-cordex"></span>

	==== 1.5.4.4 Coordinated Regional Downscaling Experiment (CORDEX) ====		==== 1.5.4.4 Coordinated Regional Downscaling Experiment (CORDEX) ====

Laura: /* 1.5.3.4 Models of Lower Complexity */

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1.5.3.4 Models of Lower Complexity

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	~~'''~~Box 1.3 \| Emissions ~~Met''' '''rics~~ in AR6 WGI~~'''~~		== Box 1.3 \| Emissions Metrics in AR6 WGI ==

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	The metrics assessed in this Report are also used, and separately assessed, by WGIII. See Cross-Chapter Box 2 and Annex B in [[IPCC:Wg1:Chapter:Chapter-2\|Chapter 2]] of the WGIII contribution to AR6.		The metrics assessed in this Report are also used, and separately assessed, by WGIII. See Cross-Chapter Box 2 and Annex B in [[IPCC:Wg1:Chapter:Chapter-2\|Chapter 2]] of the WGIII contribution to AR6.
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	<div id="1.5.4" class="h2-container"></div>		<div id="1.5.4" class="h2-container"></div>

	<span id="modelling-techniques-comparisons-and-performance-assessments"></span>		<span id="modelling-techniques-comparisons-and-performance-assessments"></span>

	=== 1.5.4 Modelling Techniques, Comparisons and Performance Assessments ===		=== 1.5.4 Modelling Techniques, Comparisons and Performance Assessments ===

Laura: /* 1.5.3.1.1 Model grids and resolution */

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1.5.3.1.1 Model grids and resolution

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	<span id="model-grids-and-resolution"></span>		<span id="model-grids-and-resolution"></span>
	===== ''1.5.3.1.1 Model grids~~'' ''~~and resolution'' =====		===== 1.5.3.1.1 Model grids and resolution =====

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	<span id="representation-of-physical-and-chemical-processes-in-esms"></span>		<span id="representation-of-physical-and-chemical-processes-in-esms"></span>

	===== 1.5.3.1.2 Representation of physical and chemical processes in ESMs =====		===== 1.5.3.1.2 Representation of physical and chemical processes in ESMs =====

Laura: /* 1.5.1.1.5 Paleoclimate */

2026-06-22T10:59:29Z

1.5.1.1.5 Paleoclimate

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	<span id="paleoclimate"></span>		<span id="paleoclimate"></span>

	===== ''1.5.1.1.5 Paleoclimate'' =====		===== 1.5.1.1.5 Paleoclimate =====

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	<span id="threats-to-observational-capacity-or-continuity"></span>		<span id="threats-to-observational-capacity-or-continuity"></span>

	==== 1.5.1.2 Threats to Observational Capacity or Continuity ====		==== 1.5.1.2 Threats to Observational Capacity or Continuity ====

Laura: /* 1.5.1.1.4 Biosphere */

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1.5.1.1.4 Biosphere

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	<span id="biosphere"></span>		<span id="biosphere"></span>

	===== ''1.5.1.1.4 Biosphere'' =====		===== 1.5.1.1.4 Biosphere =====

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	<span id="paleoclimate"></span>		<span id="paleoclimate"></span>

	===== ''1.5.1.1.5 Paleoclimate'' =====		===== ''1.5.1.1.5 Paleoclimate'' =====