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

=== 1.6.1 Scenarios ===

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A scenario is a description of how the future may develop, based on a coherent and internally consistent set of assumptions about key drivers including demography, economic processes, technological innovation, governance, lifestyles, and relationships among these driving forces ( [[#1.6.1.1|Section 1.6.1.1]] ; [[#IPCC--2000|IPCC, 2000]] ; [[#Rounsevell--2010|Rounsevell and Metzger, 2010]] ; [[#O’Neill--2014|O’Neill et al., 2014]] ). Scenarios can also be defined by geophysical driving forces only, such as emissions or abundances of GHGs, aerosols, and aerosol precursors or land-use patterns. Scenarios are not predictions; instead, they provide a ‘what-if’ investigation of the implications of various developments and actions ( [[#Moss--2010|Moss et al., 2010]] ). WGI investigates potential future climate change principally by assessing climate model simulations using emissions scenarios originating from the WGIII community ( [[#1.6.1.2|Section 1.6.1.2]] ). The scenarios used in this WGI Report cover various hypothetical ‘baseline scenarios’ or ‘reference futures’ that could unfold in the absence of any – or any additional – climate policies (Glossary). These ‘reference scenarios’ originate from a comprehensive analysis of a wide array of socio-economic drivers, such as population growth, technological development, and economic development, and their broad spectrum of associated energy, land use and emissions implications ( [[#Riahi--2017|Riahi et al., 2017]] ). With direct policy relevance to the Paris Agreement’s 1.5°C and ‘well below’ 2°C goals, this Report also assesses climate futures where the effects of additional climate change mitigation action are explored, i.e., so-called mitigation scenarios (for a broader discussion of scenarios and futures analysis, see Cross-Chapter Box 1, Table 1 in SRCCL, [[#IPCC--2019a|IPCC, 2019a]] ).

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[[File:508c1866fab62f95ebd51adbefe33da6 IPCC_AR6_WGI_Figure_1_24.png]]

'''Figure 1.24 |''' '''The dimensions of integration across chapters and Working Groups in the IPCC AR6 Assessment.''' This Report adopts three explicit dimensions of integration to integrate knowledge across chapters and Working Groups. The first dimension is scenarios; the second dimension is global mean warming levels relative to pre-industrial levels; and the third dimension is cumulative CO <sub>2</sub> emissions. For the scenarios, illustrative 2100 end-points are also indicated (white circles). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

For this Report, the main emissions, concentration and land-use scenarios considered are a subset of scenarios recently developed using the Shared Socio-economic Pathways framework (SSPs; [[#1.6.1.1|Section 1.6.1.1]] and Cross-Chapter Box 1.4; [[#Riahi--2017|Riahi et al., 2017]] ). Initially, the term ‘SSP’ described five broad narratives of future socio-economic development only ( [[#O’Neill--2014|O’Neill et al., 2014]] ). However, at least in the WGI community, the term ‘SSP scenario’ is now more widely used to refer directly to future emissions and concentration scenarios that result from combining these socio-economic development pathways with climate change mitigation assumptions. These are assessed in detail in WGIII (AR6 WGIII Chapter 3) and in Cross-Chapter Box 1.4, Table 1 in this chapter.

This Report uses a core set of five illustrative SSP scenarios to assist cross-Chapter integration and cross-Working Group applications: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 (Cross-Chapter Box 1.4, Table 1). These scenarios span a wide range of plausible societal and climatic futures from potentially below 1.5°C best-estimate warming to over 4°C warming by 2100 (Figure 1.25). The set of five SSP scenarios includes those in ‘Tier 1’ simulations of the CMIP6 ScenarioMIP intercomparison project ( [[#1.5.4|Section 1.5.4]] ; [[#O’Neill--2016|O’Neill et al., 2016]] ) that participating climate modelling groups were asked to prioritize (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), plus the low emissions scenario SSP1-1.9. SSP1-1.9 is used in combination with SSP1-2.6 to explore differential outcomes of approximately 1.5°C and 2.0°C warming relative to pre-industrial levels, relevant to the Paris Agreement goals. Further SSP scenarios are used in this report to assess specific aspects of, for example, air pollution policies in [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Cross-Chapter Box 1.4). In addition, the previous generation of Representative Concentration Pathways (RCPs) is also used in this Report when assessing future climate change ( [[#1.6.1.3|Section 1.6.1.3]] and Cross-Chapter Box 1.4, Table 1).

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[[File:02d01325d67a7cc0d3cbcb98198c0679 IPCC_AR6_WGI_Figure_1_25.png]]

'''Figure 1.25 |''' '''Global mean surface air temperature (GSAT) illustrated as warming stripes from blue (cold) to red (warm) over three different time periods''' . From 1750–1850 based on PAGES 2K reconstructions ( [[#PAGES%202k%20Consortium--2017|PAGES 2k Consortium, 2017]] , 2019); from 1850–2018 showing the composite GSAT time series assessed in Chapter 2; and from 2020 onwards using the assessed GSAT projections for each Shared Socio-economic Pathway (SSP) (from Chapter 4). For the projections, the upper end of each arrow aligns with the colour corresponding to the 95th percentile of the projected temperatures and the lower end aligns with the colour corresponding to the 5th percentile of the projected temperature range. Projected temperatures are shown for five scenarios from ‘very low’ SSP1-1.9 to ‘very high’ SSP5-8.5 (see Cross-Chapter Box 1.4 for more details on the scenarios). For illustrative purposes, natural variability has been added from a single CMIP6 Earth system model (MRI ESM2). The points in time when total CO <sub>2</sub> emissions peak; reach halved levels of the peak; and reach net zero emissions are indicated with arrows, ‘½’ and ‘0’ marks, respectively. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

Climatic changes over the 21st century (and beyond) are projected and assessed in subsequent chapters, using a broad range of climate models, conditional on the various SSP scenarios. The projected future changes can then be put into the context of longer-term paleoclimate data and historical observations, showing how the higher emissions and higher concentration scenarios diverge further from the range of climate conditions that ecosystems and human societies experienced in the past 2000 years in terms of global mean temperature and other key climate variables (Figures 1.26 and 1.5).

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[[File:2b29770b30fc6f9ebd46149aa4a6238d IPCC_AR6_WGI_Figure_1_26.png]]

'''Figure 1.26 |''' '''Historical and projected future concentrations of carbon dioxide (CO''' <sub>2</sub> '''), methane (CH''' <sub>4</sub> ''') and nitrous oxide (N''' <sub>2</sub> '''O) and global mean temperatures (GMST).''' GMST temperature reconstructions over the last 2000 years were compiled by the PAGES 2k Consortium (2017, 2019) (grey line, with 95% uncertainty range), joined by historical GMST time series assessed in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] (black line) – both referenced against the 1850–1900 period. Future GSAT temperature projections are from CMIP6 ESM models across all concentration-driven SSP scenario projections (Chapter 4). The discontinuity around year 2100 for CMIP6 temperature projections results from the fact that not all ESM models ran each scenario past 2100. The grey vertical band indicates the future 2015–2300 period. The concentrations used to drive CMIP6 Earth system models are derived from ice core, firn and instrumental datasets ( [[#Meinshausen--2017|Meinshausen et al., 2017]] ) and projected using an emulator (Cross-Chapter Box 7.1; [[#Meinshausen--2020|Meinshausen et al., 2020]] ). The colours of the lines indicate the SSP scenarios used in this Report (Cross-Chapter Box 1.4, Figure 1). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

While scenarios are a key tool for integration across IPCC Working Groups, they also allow the integration of knowledge among scientific communities and across time scales. For example, agricultural yield, infrastructure and human health impacts of increased drought frequency, extreme rainfall events and hurricanes are often examined in isolation. New insights on climate impacts in WGII can be gained if compound effects of multiple cross-sectoral impacts are considered across multiple research communities under consistent scenario frameworks (Section 11.8; [[#Leonard--2014|Leonard et al., 2014]] ; [[#Warszawski--2014|Warszawski et al., 2014]] ). Similarly, a synthesis of WGI knowledge on sea level rise contributions is enabled by a consistent application of future scenarios across all specialized research communities, such as ice-sheet mass balance analyses, glacier loss projections and thermosteric change from ocean heat uptake (Chapter 9; e.g. [[#Kopp--2014|Kopp et al., 2014]] ).

Inaddition to the comprehensive SSP scenario set and the RCPs, multiple idealized scenarios and time-slice experiments using climate models are assessed in this Report. Idealized scenarios refer to experiments where, for example, CO <sub>2</sub> concentrations are increased by 1% per year, or instantly quadrupled. Such idealized experiments have been extensively used in previous model intercomparison projects and constitute the core ‘DECK’ set of model experiments of CMIP6 ( [[#1.5.4|Section 1.5.4]] ). They are, for example, used to diagnose the patterns of climate feedbacks across the suite of models assessed in this Report (Chapter 7).

In the following section, we further introduce the SSP scenarios and how they relate to the Shared Socio-economic Pathways framework ( [[#1.6.1.1|Section 1.6.1.1]] ); describe the scenario generation process ( [[#1.6.1.2|Section 1.6.1.2]] ); and provide a historical review of scenarios used in IPCC assessment reports ( [[#1.6.1.3|Section 1.6.1.3]] ); before briefly discussing questions of scenario likelihood, scenario uncertainty and the use of scenario storylines ( [[#1.6.1.4|Section 1.6.1.4]] ).

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==== 1.6.1.1 Shared Socio-economic Pathways ====

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The Shared Socio-economic Pathways SSP1 to SSP5 describe a range of plausible trends in the evolution of society over the 21st century. They were developed in order to connect a wide range of research communities ( [[#Nakicenovic--2014|Nakicenovic et al., 2014]] ) and consist of two main elements: a set of qualitative, narrative storylines describing societal futures ( [[#O’Neill--2017a|O’Neill et al., 2017a]] ) and a set of quantified measures of development at aggregated and/or spatially resolved scales. Each pathway is an internally consistent, plausible and integrated description of a socio-economic future, but these socio-economic futures do not account for the effects of climate change, and no new climate policies are assumed. The SSPs’ quantitative projections of socio-economic drivers include population, gross domestic product (GDP) and urbanization ( [[#Dellink--2017|Dellink et al., 2017]] ; [[#Jiang--2017|Jiang and O’Neill, 2017]] ; [[#Samir--2017|Samir and Lutz, 2017]] ). By design, the SSPs differ in terms of the socio-economic challenges they present for climate change mitigation and adaptation ( [[#Rothman--2014|Rothman et al., 2014]] ; [[#Schweizer--2014|Schweizer and O’Neill, 2014]] ) and the evolution of these drivers within each SSP reflects this design. Broadly, the five SSPs represent ‘sustainability’ (SSP1), a ‘middle-of-the-road’ path (SSP2), ‘regional rivalry’ (SSP3), ‘inequality’ (SSP4), and ‘fossil fuel-intensive’ development (SSP5; Cross-Chapter Box 1.4, Figure 1; [[#O’Neill--2017a|O’Neill et al., 2017a]] ). More specific information on the SSP framework and the assumptions underlying the SSPs will be provided in the IPCC WGIII report (WGIII Chapter 3; see also Box SPM.1 in SRCCL ( [[#IPCC--2019d|IPCC, 2019d]] )).

The SSP narratives and drivers were used to develop scenarios of energy use, air pollution control, land use, and GHG emissions developments using integrated assessment models (IAMs; [[#Riahi--2017|Riahi et al., 2017]] ; [[#Rogelj--2018a|Rogelj et al., 2018a]] ). An IAM can derive multiple emissions futures for each socio-economic development pathway, assuming no new mitigation policies or various levels of additional mitigation action (in the case of reference scenarios and mitigation scenarios, respectively; [[#Riahi--2017|Riahi et al., 2017]] ). By design, the evolution of drivers and emissions within the SSP scenarios do not take into account the effects of climate change.

The SSPX-Y scenarios and the RCP scenarios are categorized similarly, by reference to the approximate radiative forcing levels each one entails at the end of the 21st century. For example, the ‘1.9’ in the SSP1-1.9 scenario stands for an approximate radiative forcing level of 1.9 W m <sup>–2</sup> in 2100. The first number (X) in the ‘SSPX-Y’ acronym refers to one of the five shared socio-economic development pathways (Cross-Chapter Box 1.4, Figure 1 and Table 1.4).

'''Table 1.4 | Overview of different RCP and SSP acronyms as used in this report.'''

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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]] ).

The SSP scenarios can be used for either emissions- or concentration-driven model experiments (Cross-Chapter Box 1.4). ESMs can be run with emissions and concentrations data for GHGs and aerosols and land-use or landcover maps and calculate levels of radiative forcing internally. The radiative forcing labels of the RCP and SSP scenarios, such as ‘2.6’ in RCP2.6 or SSP1-2.6, are thus approximate labels for the year 2100 only. The actual global mean effective radiative forcing varies across ESMs due to different radiative transfer schemes, uncertainties in aerosol–cloud interactions, and different feedback mechanisms, among other reasons. Nonetheless, using approximate radiative forcing labels is advantageous because it establishes a clear categorization of scenarios, with multiple climate forcings and different combinations in those scenarios summarized in a single number. The classifications according to cumulative carbon emissions ( [[#1.6.3|Section 1.6.3]] ) and global warming level ( [[#1.6.2|Section 1.6.2]] and Cross-Chapter Box 7.1 on emulators) complement those forcing labels.

A key advance of the SSP scenarios relative to the RCPs is a wider span of assumptions on future air-quality mitigation measures, and hence emissions of short-lived climate forcers (SLCFs; [[#Rao--2017|Rao et al., 2017]] ; [[#Lund--2020|Lund et al., 2020]] ). This allows for a more detailed investigation into the relative roles of GHG and SLCF emissions in future global and regional climate change, and hence the implications of policy choices. For instance, SSP1-2.6 builds on an assumption of stringent air-quality mitigation policy, leading to rapid reductions in particle emissions, while SSP3-7.0 assumes slow improvements, with pollutant emissions over the 21st century comparable to current levels (Figure 6.19 and Cross-Chapter Box 1.4, Figure 2).

One limitation of the SSP scenarios used for CMIP6 and in this Report is that they reduce emissions from all the major ozone-depleting substances controlled under the Montreal Protocol (CFCs, halons, and hydrochlorofluorocarbons (HCFCs)) uniformly, rather than representing a fuller range of possible high- and low-emissions futures ( [[#UNEP--2016|UNEP, 2016]] ). Hydrofluorocarbon (HFC) emissions, on the other hand, span a wider range within the SSPs than in the RCPs (Cross-Chapter Box 1.4, Figure 2).

The SSP scenarios and previous RCP scenarios are not directly comparable. First, the gas-to-gas compositions differ; for example, the SSP5-8.5 scenario has higher CO <sub>2</sub> concentrations but lower CH <sub>4</sub> concentrations compared to RCP8.5. Second, the projected 21st-century trajectories may differ, even if they result in the same radiative forcing by 2100. Third, the overall effective radiative forcing (Chapter 7) may differ, and tends to be higher for the SSPs compared to RCPs that share the same nominal stratospheric-temperature-adjusted radiative forcing label. The stratospheric-temperature-adjusted radiative forcings of the SSPs and RCPs, however, remain relatively close, at least by 2100 ( [[#Tebaldi--2021|Tebaldi et al., 2021]] ). In summary, differences in, for example, CMIP5 RCP8.5 and CMIP6 SSP5-8.5 ESM outputs, are partially due to different scenario characteristics rather than different ESM characteristics only ( [[IPCC:Wg1:Chapter:Chapter-4#4.6.2|Section 4.6.2]] ).

When investigating various mitigation futures, WGIII goes beyond the core set of SSP scenarios assessed in WGI (SSP1-1.9, SSP1-2.6, etc.) to consider the characteristics of more than 1000 scenarios (Cross-Chapter Box 7.1). In addition, while staying within the framework of socio-economic development pathways (SSP1 to SSP5), WGIII also considers various mitigation possibilities through so-called illustrative pathways (IPs). These illustrative pathways help to highlight key narratives in the literature concerning various technological, social and behavioural options for mitigation, various timings for implementation, or varying emphasis on different GHG and land-use options. Just as with the SSPX-Y scenarios considered in this Report, these illustrative pathways can be placed in relation to the matrix of SSP families and approximate radiative forcing levels in 2100 (Cross-Chapter Box 1.4, Figure 1; IPCC WGIII, Chapter 3).

No likelihood is attached to the scenarios assessed in this report, and the feasibility of specific scenarios in relation to current trends is best informed by the WGIII contribution to AR6. In the scenario literature, the plausibility of the high emissions levels underlying scenarios such as RCP8.5 or SSP5-8.5 has been debated in light of recent developments in the energy sector ( [[#1.6.1.4|Section 1.6.1.4]] ).

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

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The scenario generation process involves research communities linked to all three IPCC Working Groups (Figure 1.27). It generally starts in the scientific communities associated with WGII and WGIII with the definition of new socio-economic scenario storylines ( [[#IPCC--2000|IPCC, 2000]] ; [[#O’Neill--2014|O’Neill et al., 2014]] ) that are quantified in terms of their drivers – i.e., GDP, population, technology, energy and land use – and their resulting emissions ( [[#Riahi--2017|Riahi et al., 2017]] ). Then, numerous complementation and harmonization steps are necessary for datasets within the WGI and WGIII science communities, including gridding emissions of anthropogenic short-lived forcers, providing open biomass-burning emissions estimates, preparing land-use patterns, aerosol fields, stratospheric and tropospheric ozone, nitrogen deposition datasets, solar irradiance and aerosol optical property estimates, and observed and projected GHG concentration time series (documented for CMIP6 through input4mips; Cross-Chapter Box 1.4, Table 2; [[#Durack--2018|Durack et al., 2018]] ).

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'''Figure 1.27 |''' '''A simplified illustration of the scenario generation process, involving the scientific communities represented in the three IPCC Working Groups.''' The circular set of arrows at the top indicates the main set of models and workflows used in the scenario generation process, with the lower level indicating the datasets.

Once these datasets are completed, ESMs are run in coordinated model intercomparison projects in the WGI science community, using standardized simulation protocols and scenario data. The most recent example of such a coordinated effort is the CMIP6 exercise ( [[#1.5.4|Section 1.5.4]] ; [[#Eyring--2016|Eyring et al., 2016]] ) with, in particular, ScenarioMIP ( [[#O’Neill--2016|O’Neill et al., 2016]] ). The WGI science community feeds back climate information to WGIII via climate emulators (Cross-Chapter Box 7.1) that are updated and calibrated with the ESMs’ temperature responses and other lines of evidence. Next, this climate information is used to compute several high-level global climate indicators (e.g., atmospheric concentrations, global temperatures) for a much wider set of hundreds of scenarios that are assessed as part of the IPCC WGIII Assessment (WGIII Annex C). The outcomes from climate models run under the different scenarios are then used to calculate the evolution of climatic impact-drivers (Chapter 12), and utilized by impact researchers together with exposure and vulnerability information, in order to characterize risk to human and natural systems from future climate change. The climate impacts associated with these scenarios or different warming levels are then assessed as part of WGII reports (Figure 1.27).

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==== 1.6.1.3 History of Scenarios within the IPCC ====

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Scenario modelling experiments have been a core element of physical climate science since the first transient simulations with a general circulation model in 1988 ( [[#1.3|Section 1.3]] ; [[#Hansen--1988|Hansen et al., 1988]] ). Scenarios and modelling experiments assessed in IPCC reports have evolved over time, which provides a ‘history of how the future was seen’. The starting time for the scenarios moves as actual emissions supersede earlier emissions assumptions, while new scientific insights into the range of plausible population trends, behavioural changes and technology options and other key socio-economic drivers of emissions also emerge (see WGIII; [[#Leggett--1992|Leggett et al., 1992]] ; [[#IPCC--2000|IPCC, 2000]] ; [[#Moss--2010|Moss et al., 2010]] ; [[#Riahi--2017|Riahi et al., 2017]] ). Many different sets of climate projections have been produced over the past several decades, using different sets of scenarios. Here, we compare those earlier scenarios against the most recent ones.

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[[File:2601c5b5fc2fb8f911fa3dd12c7e83cc IPCC_AR6_WGI_Figure_1_28.png]]

'''Figure 1.28 |''' '''Comparison of the range of fossil fuel and industrial CO''' <sub>2</sub> '''emissions from scenarios used in previous assessments up to AR6.''' Previous assessments are the IS92 scenarios from 1992 '''(top)''' , the Special Report on Emissions Scenarios (SRES) scenarios from the year 2000 '''(second panel)''' , the Representative Concentration Pathway (RCP) scenarios designed around 2010 '''(third panel)''' and the Shared Socio-economic Pathways (SSP) scenarios '''(fourth panel)''' . In addition, historical emissions are shown (black line; Figure 5.5); a more complete set of scenarios is assessed in SR1.5 '''(bottom)''' ; ( [[#Huppmann--2018|Huppmann et al., 2018]] ). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

Climate science research involving scenarios necessarily follows a series of consecutive steps (Figure 1.27). As each step waits for input from the preceding one, delays often occur that result in the impact literature basing its analyses on earlier scenarios than those most current in the climate change mitigation and climate system literature. It is therefore important to provide an approximate comparison across the various scenario generations (Chapter 4, Figure 1.28, and Cross-Chapter Box 1.4, Table 1).

The first widely used set of IPCC emissions scenarios was the IS92 scenarios in 1992 ( [[#Leggett--1992|Leggett et al., 1992]] ). Apart from reference scenarios, IS92 also included a set of stabilization scenarios, the so-called ‘S’ scenarios. Those ‘S’ pathways were designed to lead to CO <sub>2</sub> stabilization levels such as 350 ppm or 450 ppm. By 1996, those latter stabilization levels were complemented in the scientific literature by alternative trajectories that assumed a delayed onset of climate change mitigation action (Figure 1.28; [[#Wigley--1996|Wigley et al., 1996]] ).

By 2000, the IPCC Special Report on Emissions Scenarios (SRES) produced the SRES scenarios ( [[#IPCC--2000|IPCC, 2000]] ), albeit without assuming any climate policy-induced mitigation. The four broad groups of SRES scenarios (scenario ‘families’) – A1, A2, B1 and B2 – were the first scenarios to emphasize socio-economic scenario storylines, and also first to emphasize other GHGs, land-use change and aerosols. Represented by three scenarios for the high-growth A1 scenario family, those 6 SRES scenarios (A1FI, A1B, A1T, A2, B1, and B2) can still sometimes be found in today’s climate impact literature. The void of missing climate change mitigation scenarios was filled by a range of community exercises, including the so-called ‘post-SRES scenarios’ ( [[#Swart--2002|Swart et al., 2002]] ).

The RCP scenarios ( [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ) then broke new ground by providing low-emissions pathways that implied strong climate change mitigation, including an example with negative CO <sub>2</sub> emissions on a large scale, namely RCP2.6. As shown in Figure 1.28, the upper end of the scenario range has not substantially shifted. Building on the SRES multi-gas scenarios, the RCPs include time series of emissions and concentrations of the full suite of GHGs, aerosols and chemically active gases, as well as land use and land cover ( [[#Moss--2010|Moss et al., 2010]] ). The word ‘representative’ signifies that each RCP is only one of many possible scenarios that would lead to the specific radiative forcing characteristics. The term ‘pathway’ emphasizes that not only the long-term concentration levels are of interest, but also the trajectory taken over time to reach that outcome ( [[#Moss--2010|Moss et al., 2010]] ). RCPs usually refer to the concentration pathway extending to 2100, for which IAMs produced corresponding emissions scenarios. Four RCPs produced from IAMs were selected from the published literature and are used in AR5 as well as in this report, spanning approximately the range from below 2°C warming to high (above 4°C) warming best-estimates by the end of the 21st century: RCP2.6, RCP4.5 and RCP6.0 and RCP8.5 (Cross-Chapter Box 1.4, Table 1). Extended Concentration Pathways (ECPs) describe extensions of the RCPs from 2100 to 2300 that were calculated using simple rules generated by stakeholder consultations; these do not represent fully consistent scenarios ( [[#Meinshausen--2011b|Meinshausen et al., 2011b]] ).

By design, theRCP emissions and concentrations pathways were originally developed using particular socio-economic development pathways, but those are no longer considered ( [[#Moss--2010|Moss et al., 2010]] ). The different levels of emissions and climate change represented in the RCPs can hence be explored against the backdrop of different socio-economic development pathways (SSP1 to SSP5; [[#1.6.1.1|Section 1.6.1.1]] and Cross-Chapter Box 1.4). This integrative SSP-RCP framework (‘SSPX-RCPY’ in Table 1.4) is now widely used in the climate impact and policy analysis literature (e.g., [[#ICONICS--2021|ICONICS, 2021]] ; [[#Green--2020|Green et al., 2020]] ; [[#O’Neill--2020|O’Neill et al., 2020]] ), where climate projections obtained under the RCP scenarios are analysed against the backdrop of various SSPs. Considering various levels of future emissions and climate change for each socio-economic development pathway was an evolution from the previous SRES framework ( [[#IPCC--2000|IPCC, 2000]] ), in which socio-economic and emissions futures were closely aligned.

The new set of scenarios (SSP1-1.9 to SSP5-8.5) now features a higher top level of CO <sub>2</sub> emissions (SSP5-8.5 compared to RCP8.5), although the most significant change is again the addition of a very low climate change mitigation scenario (SSP1-1.9, compared to the previous low scenario, RCP2.6). Also, historically, none of the previous scenario sets featured a scenario that involves a very pronounced peak-and-decline emissions trajectory, but SSP1-1.9 does so now. The full set of nine SSP scenarios now includes a high-aerosol-emissions scenario (SSP3-7.0). The RCPs featured more uniformly low aerosol trajectories across all scenarios (Cross-Chapter Box 1.4, Figure 2). More generally, the SSP scenarios feature a later peak of global emissions for the lower scenarios, simply as a consequence of historical emissions not having followed the trajectory projected by previous low scenarios (Figure 1.28).

Over the last decades, discussions around scenarios have often focussed on whether recent trends make certain future scenarios more or less probable or whether all scenarios are too high or too low. When the SRES scenarios first appeared, the debate was often whether the scenarios were overestimating actual world emissions developments (e.g., Castles and Henderson, 2003). With the strong emissions increase throughout the 2000s, that debate then shifted towards the question of whether the lower future climate change mitigation scenarios were rendered unfeasible ( [[#Pielke--2008|Pielke et al., 2008]] ; [[#van%20Vuuren--2008|van Vuuren and Riahi, 2008]] ). Historical emissions between 2000 and 2010 approximately track the upper half of SRES and RCP projections (Figure 1.28). More generally, the global fossil fuel and industrial CO <sub>2</sub> emissions of recent decades tracked approximately the middle of the projected scenario ranges (Figure 1.28), although with regional differences ( [[#Pedersen--2020|Pedersen et al., 2020]] ).

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==== 1.6.1.4 The Likelihood of Reference Scenarios, Scenario Uncertainty and Storylines ====

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In general, no likelihood is attached to the scenarios assessed in this Report. The use of different scenarios for climate change projections allows the exploration of ‘scenario uncertainty’ ( [[#1.4.4|Section 1.4.4]] ; SR1.5; [[#Collins--2013|Collins et al., 2013]] ). Scenario uncertainty is fundamentally different from geophysical uncertainties, which result from limitations in the understanding and predictability of the climate system ( [[#Smith--2011|Smith and Stern, 2011]] ). In scenarios, by contrast, future emissions depend to a large extent on the collective outcome of choices and processes related to population dynamics and economic activity, or on choices that affect a given activity’s energy and emissions intensity ( [[#Jones--2000|Jones, 2000]] ; [[#Knutti--2008|Knutti et al., 2008]] ; [[#Kriegler--2012|Kriegler et al., 2012]] ; [[#van%20Vuuren--2014|van Vuuren et al., 2014]] ). Even if identical socio-economic futures are assumed, the associated future emissions still face uncertainties, since different experts and model frameworks diverge in their estimates of future emissions ranges ( [[#Ho--2019|Ho et al., 2019]] ).

When exploring various climate futures, scenarios with no, or no additional, climate policies are often referred to as ‘baseline’ or ‘reference scenarios’ ( [[#1.6.1.1|Section 1.6.1.1]] and Glossary). Among the five core scenarios used most in this report, SSP3-7.0 and SSP5-8.5 are explicit ‘no-climate-policy’ scenarios (Cross-Chapter Box 1.4, Table 1; [[#Gidden--2019|Gidden et al., 2019]] ), assuming a carbon price of zero. These future ‘baseline’ scenarios are hence counterfactuals that include fewer climate policies compared to ‘business-as-usual’ scenarios – given that ‘business-as-usual’ scenarios could be understood to imply a continuation of existing climate policies. Generally, future scenarios are meant to cover a broad range of plausible futures, due, for example to unforeseen discontinuities in development pathways ( [[#Raskin--2020|Raskin and Swart, 2020]] ), or to large uncertainties in underlying long-term projections of economic drivers ( [[#Christensen--2018|Christensen et al., 2018]] ). However, the likelihood of high-emissions scenarios such as RCP8.5 or SSP5-8.5 is considered low in light of recent developments in the energy sector ( [[#Hausfather--2020a|Hausfather and Peters, 2020a]] , b). Studies that consider possible future emissions trends in the absence of additional climate policies, such as the recent IEA 2020 World Energy Outlook ‘stated policy’ scenario ( [[#IEA--2020|IEA, 2020]] ), project approximately constant fossil fuel and industrial CO <sub>2</sub> emissions out to 2070, approximately in line with the intermediate RCP4.5, RCP6.0 and SSP2-4.5 scenarios ( [[#Hausfather--2020b|Hausfather and Peters, 2020b]] ) and the 2030 global emissions levels that are pledged as part of the Nationally Determined Contributions (NDCs) under the Paris Agreement ( [[#1.2.2|Section 1.2.2]] ; [[#Fawcett--2015|Fawcett et al., 2015]] ; [[#Rogelj--2016|Rogelj et al., 2016]] ; [[#UNFCCC--2016|UNFCCC, 2016]] ; [[#IPCC--2018|IPCC, 2018]] ). On the other hand, the default concentrations aligned with RCP8.5 or SSP5-8.5 and resulting climate futures derived by ESMs could be reached by lower emissions trajectories than RCP8.5 or SSP5-8.5. That is because the uncertainty range on carbon cycle feedbacks includes stronger feedbacks than assumed in the default derivation of RCP8.5 and SSP5-8.5 concentrations (Section 5.4; [[#Ciais--2013|Ciais et al., 2013]] ; [[#Friedlingstein--2014|Friedlingstein et al., 2014]] ; [[#Booth--2017|Booth et al., 2017]] ).

To address long-term scenario uncertainties, scenario storylines (or ‘narratives’) are often used (see [[#1.4.4|Section 1.4.4]] for a more general discussion on ‘storylines’, also covering ‘physical climate storylines’; [[#Rounsevell--2010|Rounsevell and Metzger, 2010]] ; [[#O’Neill--2014|O’Neill et al., 2014]] ). Scenario storylines are descriptions of a future world, and the related large-scale socio-economic development pathways towards that world that are deemed plausible within the current state of knowledge and historical experience ( [[#1.2.3|Section 1.2.3]] ; WGIII). Scenario storylines attempt to ‘stimulate, provoke, and communicate visions of what the future could hold for us’ ( [[#Rounsevell--2010|Rounsevell and Metzger, 2010]] ) in settings where either limited knowledge or inherent unpredictability in social systems prevent a forecast or numerical prediction. Scenario storylines have been used in previous climate research, and they are the explicit or implicit starting point of any scenario exercise, including for the SRES scenarios ( [[#IPCC--2000|IPCC, 2000]] ) and the SSPs (e.g., [[#O’Neill--2017a|O’Neill et al., 2017a]] ).

Recent technological or socio-economic trends might be informative for bounding near-term future trends, for example, if technological progress renders a mitigation technology cheaper than previously assumed. However, short-term emissions trends alone do not generally rule out an opposite trend in the future ( [[#van%20Vuuren--2010|van Vuuren et al., 2010]] ). The ranking of individual RCP emissions scenarios from the IAMs with regard to emissions levels is different for different time horizons, for example, 2020 compared with longer-term emissions levels. For example, the strongest climate change mitigation scenario, RCP2.6, was in fact the second highest CO <sub>2</sub> emissions scenario (jointly with RCP4.5) before 2020 in the set of RCPs and the strong global emissions decline in RCP2.6 only followed after 2020. Implicitly, this scenario feature was cautioning against the assumption that short-term trends predicate particular long-term trajectories. This is also the case in relation to the COVID-19 related drop in 2020 emissions. Potential changes in underlying drivers of emissions, such as those potentially incentivized by COVID-19 recovery stimulus packages, are more significant for longer-term emissions than the short-term deviation from recent emissions trends (Cross-Chapter Box 6.1 on COVID-19).

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'''Cross-Chapter Box 1.4 | The SSP Scenarios as Used in Workin''' '''g 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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[[File:24bb5b5d69b887eac6b5ee61645ca628 IPCC_AR6_WGI_CCBox_1_4_Figure_1.png]]

'''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"
|-
| '''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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