Editing IPCC:AR6/WGI/Chapter-1 (section)

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

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'''Contributing Authors:''' Jan S. Fuglestvedt (Norway), Celine Guivarch (France), Christopher Jones (United Kingdom), Malte Meinshausen (Australia/Germany), Zebedee R. J. Nicholls (Australia), Gian-Kasper Plattner (Switzerland), Keywan Riahi (Austria), Joeri Rogelj (United Kingdom/Belgium), Sophie Szopa (France), Claudia Tebaldi (United States of America), Anne-Marie Treguier (France), and Detlef van Vuuren (The Netherlands)

The nine new SSP emissions and concentrations scenarios (SSP1-1.9 to SSP5-8.5; Cross-Chapter Box 1.4, Table 1) offer unprecedented detail of input data for climate model simulations. They allow for a more comprehensive assessment of climate drivers and responses than has previously been available, in particular because some of the scenarios’ time series, (e.g., pollutants, emissions or changes in land use and land cover), are more diverse in the SSP scenarios than in the RCPs used in AR5 (Cross-Chapter Box 1.4, Figure 2; e.g., [[#Chuwah--2013|Chuwah et al., 2013]]).

The core set of five illustrative SSP scenarios – SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 – was selected in this Report to align with the objective that the new generation of SSP scenarios should fill certain gaps identified in the RCPs. For example, a scenario assuming reduced air-pollution control and thus higher aerosol emissions was missing from the RCPs. Likewise, nominally the only ‘no-additional-climate-policy’ scenario in the set of RCPs was RCP8.5. The new SSP3-7.0 ‘no-additional-climate-policy’ scenario fills both these gaps. A very strong mitigation scenario in line with the 1.5°C goal of the Paris Agreement was also missing from the RCPs, and the SSP1-1.9 scenario now fills this gap, complementing the other strong mitigation scenario SSP1-2.6. The five core SSPs were also chosen to ensure some overlap with the RCP levels for radiative forcing at the year 2100 (specifically 2.6, 4.5, and 8.5; [[#O’Neill--2016|O’Neill et al., 2016]]; [[#Tebaldi--2021|Tebaldi et al., 2021]]), although effective radiative forcings are generally higher in the SSP scenarios compared to the equivalently named RCP pathways ([[IPCC:Wg1:Chapter:Chapter-4#4.6.2|Section 4.6.2]] and Cross-Chapter Box 1.4, Figure 1). In theory, running scenarios with similar radiative forcings would permit analysis of the CMIP5 and CMIP6 outcomes for pairs of scenarios (e.g., RCP8.5 and SSP5-8.5) in terms of varying model characteristics rather than differences in the underlying scenarios. In practice, however, there are limitations to this approach (Sections 1.6.1.1 and 4.6.2).

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'''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).

'''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.
{| class="wikitable"
|-
! '''SSPX-Y Scenario'''

! '''Description From an Emissions/Concentrations and Temperature Perspect''' '''ive (Table 4.2)'''

! '''Closes''' '''t RCP Scenarios'''

|-
| '''SSP1-1.9'''

| Holds warming to approximately 1.5°C above 1850–1900 in 2100 after slight overshoot (median) and implied net zero CO <sub>2</sub> emissions around the middle of the century.

| Not available. No equivalently low RCP scenario exists.

|-
| '''SSP1-2.6'''

| Stays below 2.0°C warming relative to 1850–1900 (median) with implied net zero CO <sub>2</sub> emissions in the second half of the century.

| RCP2.6, although RCP2.6 might be cooler for the same model settings.

|-
| SSP4-3.4

| A scenario between SSP1-2.6 and SSP2-4.5 in terms of end-of-century radiative forcing. It does not stay below 2.0°C in most CMIP6 runs (Chapter 4) relative to 1850–1900.

| No 3.4 level of end-of-century radiative forcing was available in the RCPs. Nominally SSP4-3.4 sits between RCP 2.6 and RCP 4.5, although SSP4-3.4 might be more similar to RCP4.5. Also, in the early decades of the 21st century, SSP4-3.4 is close to RCP6.0, which featured lower radiative forcing than RCP4.5 in those decades.

|-
| '''SSP2-4.5'''

| Scenario approximately in line with the upper end of aggregate NDC emissions levels by 2030 (Sections [[#1.2.2|1.2.2]] and [[IPCC:Wg1:Chapter:Chapter-4#4.3|4.3]]; SR1.5, ([[#IPCC--2018|IPCC, 2018]]), Box 1). CO <sub>2</sub> emissions remaining around current levels until the middle of the century. The SR1.5 assessed temperature projections for NDCs to be between 2.7°C and 3.4°C by 2100 ([[#1.2.2|Section 1.2.2]]; SR1.5 ([[#IPCC--2018|IPCC, 2018]]); Cross-Chapter Box 11.1), corresponding to the upper half of projected warming under SSP2-4.5 (Chapter 4). New or updated NDCs by the end of 2020 did not significantly change the emissions projections up to 2030, although more countries adopted 2050 net zero targets in line with SSP1-1.9 or SSP1-2.6. The SSP2-4.5 scenario deviates mildly from a ‘no-additional-climate-policy’ reference scenario, resulting in a best-estimate warming around 2.7°C by the end of the 21st century relative to 1850–1900 (Chapter 4).

| RCP4.5 and, until 2050, also RCP6.0. Forcing in the latter was even lower than RCP4.5 in the early decades of the 21st century.

|-
| SSP4-6.0

| The end-of-century nominal radiative forcing level of 6.0 W m <sup>–2</sup> can be considered a ‘no-additional-climate-policy’ reference scenario, under SSP1 and SSP4 socio-economic development narratives.

| RCP6.0 is nominally closest in the second half of the century, although global mean temperatures are estimated to be generally lower in RCPs compared to SSPs. Furthermore, RCP6.0 features lower warming than SSP4-6.0, as it has very similar temperature projections compared to the nominally lower RCP4.5 scenario in the first half of the century.

|-
| '''SSP3-7.0'''

| An intermediate-to-high reference scenario resulting from no additional climate policy under the SSP3 socio-economic development narrative. CO <sub>2</sub> emissions roughly double from current levels by 2100. SSP3-7.0 has particularly high non-CO <sub>2</sub> emissions, including high aerosols emissions.

| Between RCP6.0 and RCP8.5, although SSP3-7.0 non-CO <sub>2</sub> emissions and aerosols are higher than in any of the RCPs.

|-
| SSP3-7.0-lowNTCF

| A variation of the intermediate-to-high reference scenario SSP3-7.0 but with mitigation of CH <sub>4</sub> and/or short-lived species such as black carbon and other short-lived climate forcers (SLCF). Note that variants of SSP3-7.0-lowNTCF differ in terms of whether CH <sub>4</sub> emissions are reduced <sup>a</sup> (Sections 4.4 and 6.6).

| SSP3-7.0-lowNTCF is between RCP6.0 and RCP8.5, as RCP scenarios generally incorporated a narrow and comparatively low level of SLCF emissions across the range of RCPs.

|-
| SSP5-3.4-OS (Overshoot)

| A mitigation-focused variant of SSP5-8.5 that initially follows unconstrained emissions growth in a fossil fuel-intensive setting until 2040 and then implements the largest net negative CO <sub>2</sub> emissions of all SSP scenarios in the second half of 21st century to reach SSP1-2.6 forcing levels in the 22nd century. Used to consider reversibility and strong overshoot scenarios in, or example, Chapters 4 and 5.

| Not available. Initially, until 2040, similar to RCP8.5.

|-
| '''SSP5-8.5'''

| A high-reference scenario with no additional climate policy. CO <sub>2</sub> emissions roughly double from current levels by 2050. Emissions levels as high as SSP5-8.5 are not obtained by integrated assessment models (IAMs) under any of the SSPs other than the fossil-fuelled SSP5 socio-economic development pathway.

| RCP8.5, although CO <sub>2</sub> emissions under SSP5-8.5 are higher towards the end of the century (Cross-Chapter Box 1.4, Figure 2). CH <sub>4</sub> emissions under SSP5-8.5 are lower than under RCP 8.5. When used with the same model settings, SSP5-8.5 may result in slightly higher temperatures than RCP8.5 ([[IPCC:Wg1:Chapter:Chapter-4#4.6.2|Section 4.6.2]]).

|}

<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 |''' '''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)]].
{| class="wikitable"
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| '''Climate Forcer'''

| '''Description'''

|-
| CO <sub>2</sub> Emissions (emissions-driven runs only)

| Harmonized historical and future gridded emissions of anthropogenic CO <sub>2</sub> emissions ([[#Hoesly--2018|Hoesly et al., 2018]]; [[#Gidden--2019|Gidden et al., 2019]]) are used instead of the prescribed CO <sub>2</sub> concentrations. See ([[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] ([[IPCC:Wg1:Chapter:Chapter-4#4.3.1|Section 4.3.1]]).

|-
| Historical and Future GHG Concentrations

| GHG surface air mole fractions of 43 species, including CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O, HFCs, PFCs, halons, HCFCs, CFCs, sulphur hexafluoride (SF <sub>6</sub>), ammonia (NF <sub>3</sub>), including latitudinal gradients and seasonality from year 1 to 2500 ([[#Meinshausen--2017|Meinshausen et al., 2017]], 2020).

|-
| Land-Use Change and Management Patterns

| Globally gridded land use- and land cover-change datasets ([[#Hurtt--2020|Hurtt et al., 2020]]; [[#Ma--2020|Ma et al., 2020]])

|-
| Biomass Burning Emissions

| Historical fire-related gridded emissions, including sulphur dioxide (SO <sub>2</sub>), nitrogen oxides (NO <sub>x</sub>), carbon monoxide (CO), black carbon (BC), organic carbon (OC), NH <sub>3</sub> , non-methane volatile organic compounds (NMVOCs), relevant to concentration-driven historical and future SSP scenario runs ([[#van%20Marle--2017|van Marle et al., 2017]]).

|-
| Stratospheric and Tropospheric Ozone

| Historical and future ozone dataset, also with total column ozone ([[#CCMI--2021|CCMI, 2021]]).

|-
| Reactive Gas Emissions

| Gridded global anthropogenic emissions of reactive gases and aerosol precursors, including CO, SO <sub>x</sub> , CH <sub>4,</sub> NO <sub>x</sub> , NMVOCs, or NH <sub>3</sub> ([[#Hoesly--2018|Hoesly et al., 2018]]; [[#Feng--2020|Feng et al., 2020]]).

|-
| Solar Forcing

| Radiative and particle input of solar variability from 1850 through to 2300 ([[#Matthes--2017|Matthes et al., 2017]]). Future variations in solar forcing also reflect long-term multi-decadal trends.

|-
| Volcanic Forcing

| Historical stratospheric aerosol climatology ([[#Thomason--2018|Thomason et al., 2018]]), with the mean stratospheric volcanic aerosol prescribed in future projections.

|}

In contrast to stylized assumptions about the future evolution of emissions (e.g., a linear phase-out from year A to year B), these SSP scenarios are the result of a detailed scenario generation process (Sections 1.6.1.1 and 1.6.1.2). While IAMs produce internally consistent future-emissions time series for CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O, and aerosols for the SSP scenarios ([[#Riahi--2017|Riahi et al., 2017]]; [[#Rogelj--2018a|Rogelj et al., 2018a]]), these emissions scenarios are subject to several processing steps for harmonization ([[#Gidden--2018|Gidden et al., 2018]]) and in-filling ([[#Lamboll--2020|Lamboll et al., 2020]]), before also being complemented by several datasets so that ESMs can run these SSPs ([[#Durack--2018|Durack et al., 2018]]; [[#Tebaldi--2021|Tebaldi et al., 2021]]). Although five scenarios are the primary focus of WGI, a total of nine SSP scenarios have been prepared with all the necessary detail to drive the ESMs as part of the CMIP6 (Cross-Chapter Box 1.4, Figure 1 and Table 2).

ESMs are driven by either emissions or concentrations scenarios. Inferring concentration changes from emissions time series requires using carbon cycle and other gas cycle models. To aid comparability across ESMs, and in order to allow participation of ESMs that do not have coupled carbon and other gas cycle models in CMIP6, most of the CMIP6 ESM experiments are so-called ‘concentration-driven’ runs, with concentrations of CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O and other well-mixed GHGs prescribed in conjunction with aerosol emissions, ozone changes and effects from human-induced land-cover changes that may be radiatively active via albedo changes (Cross-Chapter Box 1.4, Figure 2). In these concentration-driven climate projections, the uncertainty in projected future climate change resulting from our limited understanding of how the carbon cycle and other gas cycles will evolve in the future is not captured. For example, when deriving the default concentrations for these scenarios, permafrost and other carbon cycle feedbacks are considered using default settings, with a single time series prescribed for all ESMs ([[#Meinshausen--2020|Meinshausen et al., 2020]]). Thus, associated uncertainties ([[#Joos--2013|Joos et al., 2013]]; [[#Schuur--2015|Schuur et al., 2015]]) are not considered.

The so-called ‘emissions-driven’ experiments ([[#Jones--2016|Jones et al., 2016]]) use the same input datasets as concentration-driven ESM experiments, except that they use CO <sub>2</sub> emissions rather than concentrations ([[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] and [[IPCC:Wg1:Chapter:Chapter-4#4.3.1|Section 4.3.1]]). In these experiments, atmospheric CO <sub>2</sub> concentrations are calculated internally using the ESM interactive carbon cycle module and thus differ from the prescribed default CO <sub>2</sub> concentrations used in the concentration-driven runs. In the particular case of SSP5-8.5, the emissions-driven runs are assessed to add no significant additional uncertainty to future global surface air temperature (GSAT) projections ([[IPCC:Wg1:Chapter:Chapter-4#4.3.1|Section 4.3.1]]). However, generally, when assessing uncertainties in future climate projections, it is important to consider which elements of the cause–effect chain, from emissions to the resulting climate change, are interactively included as part of the model projections, and which are externally prescribed using default settings.

[[File:883f042913d8eddf93bc01e4f0615f69 IPCC_AR6_WGI_CCBox_1_4_Figure_2.png]]

'''Cross-Chapter Box 1.4, Figure 2 |''' '''Comparison between the Shared Socio-economic Pathways (SSP) scenarios and the Representative Concentration Pathway (RCP) scenarios in terms of their CO''' <sub>2</sub> ''', CH''' <sub>4</sub> '''and N''' <sub>2</sub> '''O atmospheric concentrations (a–c), and their global emissions of CO''' <sub>2</sub> ''', CH''' <sub>4</sub> ''', N''' <sub>2</sub> '''O, black carbon (BC), organic carbon (OC), sulphur dioxide (SO''' <sub>2</sub> '''), ammonia (NH''' <sub>3</sub> '''), nitrogen oxides (NOx), volatile organic compounds (VOC), sulphur hexafluoride (SF6), perfluorocarbons (PFCs), and hydrofluorocarbons (HFCs) (d–o).'''

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

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The global mean surface temperature change, or ‘global warming level’ (GWL), is a ‘dimension of integration’ that is highly relevant across scientific disciplines and socio-economic actors. First, global warming levels relative to pre-industrial conditions are the quantity in which the 1.5°C and ‘well below 2°C’ Paris Agreement goals were formulated. Second, global mean temperature change has been found to be almost-linearly related to a number of regional climate effects ([[#Mitchell--2000|Mitchell et al., 2000]]; [[#Mitchell--2003|Mitchell, 2003]]; [[#Tebaldi--2014|Tebaldi and Arblaster, 2014]]; [[#Seneviratne--2016|Seneviratne et al., 2016]]; [[#Li--2020|Li et al., 2020]]; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]]). Even where non-linearities are found, some regional climate effects can be considered to be almost scenario-independent for a given level of warming (Sections 4.2.4, 4.6.1, 8.5.3 and 10.4.3.1, and Cross-Chapter Box 11.1). Finally, the evolution of aggregated impacts with warming levels has been widely used and embedded in the assessment of the ‘Reasons for Concern’ (RFC) in IPCC WGII ([[#Smith--2009|Smith et al., 2009]]; [[#IPCC--2014a|IPCC, 2014a]]). The RFC framework was further expanded in SR1.5 (2018), SROCC (2019) and SRCCL (2019) by explicitly describing the differential impacts of half-degree warming steps ([[#1.4.4|Section 1.4.4]] and Cross-Chapter Box 12.1; cf. [[#King--2017|King et al., 2017]]).

In this Report, the term ‘global warming level’ refers to the categorization of global and regional climate change, associated impacts, emissions and concentrations scenarios by GMST relative to 1850–1900, which is the period used as a proxy for pre-industrial levels (Cross-Chapter Box 11.1). By default, GWLs are expressed in terms of global surface air temperature (GSAT; [[#1.4.1|Section 1.4.1]] and Cross-Chapter Box 2.3).

As SR1.5 concluded, even half-degree global mean temperature steps carry robust differences in climate impacts (Chapter 11; SR1.5, [[#IPCC--2018|IPCC, 2018]]; [[#Schleussner--2016a|Schleussner et al., 2016a]]; [[#Wartenburger--2017|Wartenburger et al., 2017]]). This Report adopts half-degree warming levels, which allows integration for climate projections, impacts, adaptation challenges and mitigation challenges within and across the three WGs. The core set of GWLs – 1.5°C, 2.0°C, 3.0°C and 4.0°C – are highlighted (Chapters 4, 8, 11, 12 and Atlas). Given that much impact analysis is based on previous scenarios, (i.e., RCPs or SRES), and climate change mitigation analysis is based on new emissions scenarios in addition to the main SSP scenarios, these GWLs assist in the comparison of climate states across scenarios and in the synthesis across the broader literature.

The transient and equilibrium states of certain global warming levels can differ in their climate impacts ([[#IPCC--2018|IPCC, 2018]]; [[#King--2020|King et al., 2020]]). Climate impacts in a ‘transient’ world relate to a scenario in which the world is continuing to warm. On the other hand, climate impacts at the same warming levels can also be estimated from equilibrium states after a (relatively) short-term stabilization by the end of the21st century or at a (near-)equilibrium state after a long-term (multi-decadal to multi-millennial) stabilization. Different methods to estimate these climate states come with challenges and limitations ([[IPCC:Wg1:Chapter:Chapter-4#4.6.1|Section 4.6.1]] and Cross-Chapter Box 11.1). First, information can be drawn from GCM or ESM simulations that ‘pass through’ the respective warming levels (as used and demonstrated in the Interactive Atlas), also called ‘epoch’ or ‘time-shift’ approaches (Sections 4.2.4 and 4.6.1; [[#Herger--2015|Herger et al., 2015]]; [[#James--2017|James et al., 2017]]; Tebaldi and [[#Knutti--2018|Knutti, 2018]]). Information from transient simulations can also be used through an empirical scaling relationship ([[#Seneviratne--2016|Seneviratne et al., 2016]], 2018; [[#Wartenburger--2017|Wartenburger et al., 2017]]) or using ‘time sampling’ approaches, as described in [[#James--2017|James et al. (2017)]]. Second, information can be drawn from large ESM ensembles with prescribed SST at particular global warming levels ([[#Mitchell--2017|Mitchell et al., 2017]]), although an underrepresentation of variability can arise when using prescribed SST temperatures (E.M. [[#Fischer--2018|]] [[#Fischer--2018|Fischer et al., 2018]]).

In order to fully derive climate impacts, warming levels will need to be complemented by additional information, such as their associated CO <sub>2</sub> concentrations (e.g., fertilization or ocean acidification), composition of the total radiative forcing (aerosols compared with GHGs, with varying regional distributions) or socio-economic conditions (e.g., to estimate societal impacts). More fundamentally, while a global warming level is a good proxy for the state of the climate (Cross-Chapter Box 11.1), it does not uniquely define a change in global or regional climate state. For example, regional precipitation responses depend on the details of the individual forcing mechanisms that caused the change ([[#Samset--2016|Samset et al., 2016]]); on whether the temperature level is stabilized or transient ([[#King--2020|King et al., 2020]]; [[#Zappa--2020|Zappa et al., 2020]]); on the vertical structure of the troposphere ([[#Andrews--2010|Andrews et al., 2010]]); and, in particular, on the global distribution of atmospheric aerosols ([[#Frieler--2012|Frieler et al., 2012]]). Another aspect is how Earth system components with century-to-millennial response time scales, such as long-term sea level rise or permafrost thaw, are affected by global mean warming. For example, sea level rise 50 years after a 1°C warming will be lower than sea level rise 150 years after that same 1°C warming (Chapter 9).

Also, forcing or response patterns that vary in time can create differences in regional climates for the same global mean warming level, or can create non-linearities when scaling patterns from one warming level to another ([[#King--2018|King et al., 2018]]), depending on whether near-term transient climate, end of the century, equilibrium climate or climate states after an initial overshoot are considered.

In spite of these challenges, and thanks to recent methodological advances in quantifying or overcoming them, global warming levels provide a robust and useful integration mechanism. They allow knowledge from various domains within WGI and across the three WGs to be integrated and communicated (Cross-Chapter Box 11.1). In this report, Chapters 4, 8, 11, 12 and the [[IPCC:Wg1:Chapter:Atlas|Atlas]] provide information specific to certain warming levels, highlighting the regional differences, but also the approximate scalability of regional climate change, that can arise from even a 0.5°C shift in global mean temperatures. Furthermore, building on WGI insights into physical climate system responses (Cross-Chapter Box 7.1), WGIII will use peak and end-of-century global warming levels to classify a broad set of scenarios.

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=== 1.6.3 Cumulative Carbon Dioxide Emissions ===

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The AR5 WGI ([[#IPCC--2013a|IPCC, 2013a]]) and SR1.5 ([[#IPCC--2018|IPCC, 2018]]) highlighted the near-linear relationship between cumulative carbon emissions and global mean warming (Sections 1.3 and 5.5). This implies that continued CO <sub>2</sub> emissions will cause further warming and changes in all components of the climate system, independent of any specific scenario or pathway. This is captured in the TCRE concept, which relates CO <sub>2</sub> -induced global mean warming to cumulative carbon emissions (Chapter 5). This Report thus uses cumulative CO <sub>2</sub> emissions to compare the climate response across scenarios, and to categorize emissions scenarios (Figure 1.29). The advantage of using cumulative CO <sub>2</sub> emissions is that it is an inherent emissions scenario characteristic rather than an outcome of the scenario-based projections, where uncertainties in the cause–effect chain – from emissions to atmospheric concentrations to temperature change – are important.

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[[File:c5a535ca6cc859a7b3d41a472cb68a08 IPCC_AR6_WGI_Figure_1_29.png]]

'''Figure 1.29 |''' '''The role of CO2 in driving future climate change in comparison to other greenhouse gases (GHGs)''' . The GHGs included here are CH <sub>4</sub> , N <sub>2</sub> O, and 40 other long-lived, well-mixed GHGs. The blue shaded area indicates the approximate forcing exerted by CO <sub>2</sub> in Shared Socio-economic Pathways (SSP) scenarios, ranging from very low SSP1-1.9 to very high SSP5-8.5 (Chapter 7). The CO <sub>2</sub> concentrations under the SSP1-1.9 scenarios reach approximately 350 ppm after 2150, while those of SSP5-8.5 exceed 2000 ppm CO <sub>2</sub> in the longer term (up to year 2300). Similar to the dominant radiative forcing share at each point in time (lower area plots), cumulative GWP-100-weighted GHG emissions happen to be closely correlated with cumulative CO <sub>2</sub> emissions, allowing policymakers to make use of the carbon budget concept in a policy context with multi-gas GHG baskets as it exhibits relatively low variation across scenarios with similar cumulative emissions until 2050 '''(inset panel)''' . Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

There is also a close relationship between cumulative total GHG emissions and cumulative CO <sub>2</sub> emissions for scenarios in the SR1.5 scenario database (Figure 1.29; [[#IPCC--2018|IPCC, 2018]]). The dominance of CO <sub>2</sub> compared to other well-mixed GHGs (Figure 1.29 and Section 5.2.4) allows policymakers to make use of the carbon budget concept (Section 5.5) in a policy context, in which GWP-weighted combinations of multiple GHGs are used to define emissions targets. A caveat is that cumulative GWP-weighted CO <sub>2</sub> equivalent emissions over the next decades do not yield exactly the same temperature outcomes as the same amount of cumulative CO <sub>2</sub> emissions, because atmospheric perturbation lifetimes of the various GHGs differ. While carbon budgets are not derived using GWP-weighted emissions baskets but rather by explicit modelling of non-CO <sub>2</sub> -induced warming (Section 5.5 and Cross-Chapter Box 7.1), the policy frameworks based on GWP-weighted emissions baskets can still make use of the insights from remaining cumulative carbon emissions for different warming levels.

Thesame cumulative CO <sub>2</sub> emissions could lead to a slightly different level of warming over time (Box 1.4). Rapid emissions followed by steep cuts and potentially net negative emissions would be characterized by a higher maximum warming and faster warming rate, compared with the same cumulative CO <sub>2</sub> emissions spread over a longer period. As further explored in the WGIII assessment, one potential limitation when presenting emissions pathway characteristics in cumulative emissions budget categories is that path dependencies and lock-in effects (e.g. today’s decisions regarding fossil fuel-related infrastructure) play an important role in long-term mitigation strategies ([[#Davis--2010|Davis et al., 2010]]; [[#Luderer--2018|Luderer et al., 2018]]). Similarly, high emissions early on might imply strongly net negative emissions ([[#Minx--2018|Minx et al., 2018]]) later on to reach the same target envelope for cumulative emissions and temperature by the end of the century (Box 1.4). This report explores options to address some of those potential issues from a WGI perspective (Sections 5.5.2 and 5.6.2).

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