Editing IPCC:AR6/WGIII/Chapter-3 (section)

=== 3.2.4 The AR6 Scenario Database ===

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To facilitate this assessment, a large ensemble of scenarios has been collected and made available through an interactive AR6 WGIII scenario database. The collection of the scenario outputs is coordinated by [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-3 Chapter 3] and expands upon the IPCC SR1.5 scenario explorer ( [[#Huppmann--2018|Huppmann et al. 2018]] ; [[#Rogelj--2018|Rogelj et al. 2018]] a). A complementary database for national pathways has been established by Chapter 4. Annex III.II.3 contains full details on how the scenario database was compiled.

The AR6 scenario database contains 3131 scenarios (Figure 3.5a). After an initial screening and quality control, scenarios were further vetted to assess if they sufficiently represented historical trends (Annex III.II.3.1). Of the initial 2266 scenarios with global scope, 1686 scenarios passed the vetting process and are assessed in this chapter. The scenarios that did not pass the vetting are still available in the database. The vetted scenarios were from over 50 different model families, or over 100 when considering all versions of the same family (Figure 3.1). The scenarios originated from over 15 different model intercomparison projects, with around one-fifth originating from individual studies (Figure 3.2). Because of the uneven distribution of scenarios from different models and projects, uncorrected statistics from the database can be misleading.

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[[File:faf24d48376532da5a55e9db29dc4c3a IPCC_AR6_WGIII_Figure_3_1.png]]

'''Figure 3.1 | Scenario counts from each model family defined as all versions under the same model’s name.'''

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[[File:194151b8847735652026edc899023068 IPCC_AR6_WGIII_Figure_3_2.png]]

'''Figure 3.2 | Scenario counts from each named project.'''

Each scenario with sufficient data is given a temperature classification using climate model emulators. Three emulators were used in the assessment: FAIR ( [[#Smith--2018|Smith et al. 2018]] ), CICERO-SCM ( [[#Skeie--2021|Skeie et al. 2021]] ), MAGICC ( [[#Meinshausen--2020|Meinshausen et al. 2020]] ). Only the

'''Table 3.1 | Classification of emissions scenarios into warming levels using MAGICC'''

{| class="wikitable"
|-
| '''Category'''

| '''Description'''

| '''WGI SSP'''

| '''WGIII IP/IMP'''

| '''Scenarios'''

|-
| '''C1: Limit warming to 1.5°C (&gt;50%) with no or limited overshoot'''

| Reach or exceed 1.5°C during the 21st century with a likelihood of ≤67%, and limit warming to 1.5°C in 2100 with a likelihood &gt;50%.

Limited overshoot refers to exceeding 1.5°C by up to about 0.1°C and for up to several decades.

| SSP1-1.9

| IMP-SP, IMP-LD, IMP-Ren

| '''97'''

|-
| '''C2: Return warming to 1.5°C (&gt;50%) after a high overshoot'''

| Exceed warming of 1.5°C during the 21st century with a likelihood of &gt;67%, and limit warming to 1.5°C in 2100 with a likelihood of &gt;50%.

High overshoot refers to temporarily exceeding 1.5°C global warming by 0.1°C–0.3°C for up to several decades.

|
| IMP-Neg a

| '''133'''

|-
| '''C3:''' '''Limit warming to 2°C (&gt;67%)'''

| Limit peak warming to 2°C throughout the 21st century with a likelihood of &gt;67%.

| SSP1-2.6

| IMP-GS

| '''311'''

|-
| '''C4: Limit warming to 2°C (&gt;50%)'''

| Limit peak warming to 2°C throughout the 21st century with a likelihood of &gt;50%.

|
| '''159'''

|-
| '''C5: Limit warming to 2.5°C (&gt;50%)'''

| Limit peak warming to 2.5°C throughout the 21st century with a likelihood of &gt;50%.

|
| '''212'''

|-
| '''C6: Limit warming to 3°C (&gt;50%)'''

| Limit peak warming to 3°C throughout the 21st century with a likelihood of &gt;50%.

| SSP2-4.5

| ModAct

| '''97'''

|-
| '''C7: Limit warming to 4°C (&gt;50%)'''

| Limit peak warming to 4°C throughout the 21st century with a likelihood of &gt;50%.

| SSP3-7.0

| CurPol

| '''164'''

|-
| '''C8: Exceed warming of 4°C (≥50%)'''

| Exceed warming of 4°C during the 21st century with a likelihood of ≥50%.

| SSP5-8.5

|
| '''29'''

|-
| '''C1, C2, C3: limit warming to 2°C (&gt;67%) or lower'''

| All scenarios in Categories C1, C2 and C3

|
| '''541'''

|}

a The Illustrative Mitigation Pathway ‘Neg’ has extensive use of carbon dioxide removal (CDR) in the AFOLU, energy and the industry sectors to achieve net negative emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the characteristics of C2 high-overshoot pathways, hence it has been placed in the C2 category. See Box SPM.1 for an introduction of the IPs and IMPs.

results of MAGICC are shown in this chapter as it adequately covers the range of outcomes. The emulators are calibrated against the behaviour of complex climate models and observation data, consistent with the outcomes of AR6 WGI (Cross-Chapter Box 7.1). The climate assessment is a three-step process of harmonisation, infilling and a probabilistic climate model emulator run (Annex III.II.2.5). Warming projections until the year 2100 were derived for 1574 scenarios, of which 1202 passed vetting, with the remaining scenarios having insufficient information (Figure 3.3 and Table 3.1). For scenarios that limit warming to 2°C or lower, the SR1.5 classification was adopted in AR6, with more disaggregation provided for higher warming levels (Table 3.1). These choices can be compared with the selection of common global warming levels (GWLs) of 1.5°C, 2°C, 3°C and 4°C to classify climate change impacts in the WGII assessment.

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[[File:a9a8821c0cd82cf390f97cb10557f394 IPCC_AR6_WGIII_Figure_3_3.png]]

'''Figure 3.3 | Of the 1686 scenarios that passed vetting, 1202 had sufficient data available to be classified according to temperature, with an uneven distribution across warming levels.'''

In addition to the temperature classification, each scenario is assigned to one of the following policy categories: (P0) diagnostic scenarios – 99 of 1686 vetted scenarios; (P1) scenarios with no globally coordinated policy (500) and (P1a) no climate mitigation efforts – 124, (P1b) current national mitigation efforts – 59, (P1c) Nationally Determined Contributions (NDCs) – 160, or (P1d) other non-standard assumptions – 153; (P2) globally coordinated climate policies with immediate action (634) and (P2a) without any transfer of emission permits – 435, (P2b) with transfers – 70; or (P2c) with additional policy assumptions – 55; (P3) globally coordinated climate policies with delayed (i.e., from 2030 onwards or after 2030) action (451), preceded by (P3a) no mitigation commitment or current national policies – 7, (P3b) NDCs – 426, (P3c) NDCs and additional policies – 18; (P4) cost-benefit analysis (CBA) – 2. The policy categories were identified using text pattern matching on the scenario metadata and calibrated on the best-known scenarios from model intercomparisons, with further validation against the related literature, reported emission and carbon price trajectories, and exchanges with modellers. If the information available is enough to qualify a policy category number but not sufficient for a subcategory, then only the number is retained (e.g., P2 instead of P2a/b/c). A suffix added after P0 further qualifies a diagnostic scenario as one of the other policy categories. To demonstrate the diversity of the scenarios, the vetted scenarios were classified into different categories along the dimensions of population, GDP, energy, and cumulative emissions (Figure 3.4). The number of scenarios in each category provides some insight into the current literature, but this does not indicate a higher probability of that category occurring in reality. For population, the majority of scenarios are consistent with the SSP2 ‘middle of the road’ category, with very few scenarios exploring the outer extremes. GDP has a slightly larger variation, but overall most scenarios are around the SSP2 socio-economic assumptions. The level of CCS and CDR is expected to change depending on the extent of mitigation, but there remains extensive use of both CDR and CCS in scenarios. CDR is dominated by bioenergy with CCS (BECCS) and sequestration on land, with relatively few scenarios using direct air capture with carbon storage (DACCS) and even less with enhanced weathering (EW) and other technologies (not shown). In terms of energy consumption, final energy has a much smaller range than primary energy as conversion losses are not included in final energy. Both mitigation and reference scenarios are shown, so there is a broad spread in different energy carriers represented in the database. Bioenergy has a number of scenarios at around 100 EJ, representing a constraint used in many model intercomparisons.

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[[File:691abbffd152ca36af1e3a1082607b94 IPCC_AR6_WGIII_Figure_3_4.png]]

'''Figure 3.4 | Histograms for key categories in the AR6 scenario database.''' Only scenarios that passed vetting are shown. For population and GDP, the SSP input data are also shown. The grey shading represents the 0–100% range (light grey), 25–75% range (dark grey), and the median is a black line. The figures with white areas are outside of the scenario range, but the axis limits are retained to allow comparability with other categories. Each sub-figure potentially has different x- and y-axis limits. Each figure also potentially contains different numbers of scenarios, depending on what was submitted to the database. Source: AR6 scenarios database.

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