Editing IPCC:AR6/WGI/Chapter-4 (section)

== 4.1 Scope and Overview of This Chapter ==

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This chapter assesses simulations of future climate change, covering both near-term and long-term global changes. The chapter assesses simulations of physical indicators of global climate change, such as global surface air temperature (GSAT), global land precipitation, Arctic sea ice area (SIA), and global mean sea level (GMSL). Furthermore, the chapter covers indices and patterns of properties and circulation not only for mean fields but also for modes of variability that have global significance. The choice of quantities to be assessed is summarized in Cross-Chapter Box 2.2 and comprises a subset of the quantities covered in Chapters 2 and 3. This chapter provides consistent coverage from near-term to long-term global changes and provides the global reference for the later chapters covering important processes and regional change.

Essential input to the simulations assessed here is provided by future scenarios of concentrations or anthropogenic emissions of radiatively active substances; the scenarios represent possible sets of decisions by humanity, without any assessment that one set of decisions is more probable to occur than any other set ( [[IPCC:Wg1:Chapter:Chapter-1#1.6|Section 1.6]] ). As in previous assessment reports, these scenarios are used for projections of future climate using global atmosphere–ocean general circulation models (AOGCMs) and Earth system models (ESMs; [[IPCC:Wg1:Chapter:Chapter-1#1.5.3|Section 1.5.3]] ); the latter include representation of various biogeochemical cycles such as the carbon cycle, the sulphur cycle, or ozone (e.g., [[#Flato--2011|Flato, 2011]] ; [[#Flato--2013|Flato et al., 2013]] ). This chapter thus provides a comprehensive assessment of the future global climate response to different future anthropogenic perturbations to the climate system.

Every projection assessment is conditioned on a particular forcing scenario. If sufficient evidence is available, a detailed probabilistic assessment of a physical climate outcome can be performed for each scenario separately. By contrast, there is no agreed-upon approach to assigning probabilities to forcing scenarios, to the point that it has been debated whether such an approach can even exist (e.g., [[#Grubler--2001|Grübler and Nakicenovic, 2001]] ; [[#Schneider--2001|Schneider, 2001]] , 2002). Although there were some recent attempts to ascribe subjective probabilities to scenarios (e.g., [[#Ho--2019|Ho et al., 2019]] ; [[#Hausfather--2020|Hausfather and Peters, 2020]] ), and although ‘feasibility’ along different dimensions is an important concept in scenario research (see AR6 WGIII Chapter 3), the scenarios used for the model-based projections assessed in this chapter do not come with statements about their likelihood of actually unfolding in the future. Therefore, it is usually not possible to combine responses to individual scenarios into an overall probabilistic statement about expected future climate. Exceptions to this limit in the assessment are possible only under special circumstances, such as for some statements about near-term climate changes that are largely independent of the scenario chosen (e.g., [[#4.4.1|Section 4.4.1]] ). Beyond this, no combination of responses to different scenarios can be assessed in this chapter but may be possible in future assessments.

A central element of this chapter is a comprehensive assessment of the sources of uncertainty of future projections ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.3|Section 1.4.3]] ). Uncertainty can be broken down into scenario uncertainty, model uncertainty involving model biases, uncertainty in simulated effective radiative forcing and model response, and the uncertainty arising from internal variability ( [[#Cox--2007|Cox and Stephenson, 2007]] ; [[#Hawkins--2009|Hawkins and Sutton, 2009]] ). An additional source of projection uncertainty arises from possible future volcanic eruptions and future solar variability. Assessment of uncertainty relies on multi-model ensembles such as the Coupled Model Intercomparison Project Phase 6 (CMIP6, [[#Eyring--2016|Eyring et al., 2016]] ), single-model initial-condition large ensembles (e.g., [[#Kay--2015|Kay et al., 2015]] ; [[#Deser--2020|Deser et al., 2020]] ), and ensembles initialized from the observed climate state (decadal predictions, e.g., [[#Smith--2013a|Smith et al., 2013a]] ; [[#Meehl--2014|Meehl et al., 2014]] ; [[#Boer--2016|Boer et al., 2016]] ; [[#Marotzke--2016|Marotzke et al., 2016]] ). Ensemble evaluation methods include assessment of model performance and independence (e.g., [[#Knutti--2017|Knutti et al., 2017]] ; [[#Boé--2018|Boé, 2018]] ; [[#Abramowitz--2019|Abramowitz et al., 2019]] ); emergent and other observational constraints (e.g., [[#Allen--2002|Allen and Ingram, 2002]] ; [[#Hall--2006|Hall and Qu, 2006]] ; [[#Cox--2018|Cox et al., 2018]] ); and the uncertainty assessment of equilibrium climate sensitivity and transient climate response in Chapter 7. Ensemble evaluation is assessed in Box 4.1 through the inclusion of lines of evidence in addition to the projection ensembles, including implications for potential model weighting.

The uncertainty assessment in this chapter builds on one particularly noteworthy advance since the IPCC Fifth Assessment Report (AR5). Internal variability, which constitutes irreducible uncertainty over much of the time horizon considered here ( [[#Hawkins--2016|Hawkins et al., 2016]] ; [[#Marotzke--2019|Marotzke, 2019]] ), can be better estimated in models even under a changing climate through the use of large initial-condition ensembles ( [[#Kay--2015|Kay et al., 2015]] ). For many climate quantities and compared to the forced climate change signal, internal variability is dominant in any individual realization – including the one that will unfold in reality – in the near term ( [[#Kirtman--2013|Kirtman et al., 2013]] ; [[#Marotzke--2015|Marotzke and Forster, 2015]] ), is substantial in the mid-term, and is still recognizable in the long term in many quantities ( [[#Deser--2012a|Deser et al., 2012a]] ; [[#Marotzke--2015|Marotzke and Forster, 2015]] ). This chapter will use the strengthened information on internal variability throughout.

The expanded treatment of uncertainty allows this chapter a more comprehensive assessment of the benefits from mitigation than in previous IPCC reports, as well as the climate response to carbon dioxide removal (CDR) and solar radiation modification (SRM), and how to detect them against the backdrop of internal variability. Important advances have been made in the detection and attribution of mitigation, CDR, and SRM ( [[#Burger--2015|Bürger and Cubasch, 2015]] ; [[#Lo--2016|Lo et al., 2016]] ; [[#Ciavarella--2017|Ciavarella et al., 2017]] ); exploring the ‘time of emergence’ (ToE; see Annex VII: Glossary) of responses to assumed emissions reductions ( [[#Tebaldi--2013|Tebaldi and Friedlingstein, 2013]] ; [[#Samset--2020|Samset et al., 2020]] )and the attribution of decadal events to forcing changes that reflect emissions reductions ( [[#Marotzke--2019|Marotzke, 2019]] ; [[#Spring--2020|Spring et al., 2020]] ; [[#McKenna--2021|McKenna et al., 2021]] ).

The question of the potential crossing of thresholds relative to global temperature goals ( [[#Geden--2017|Geden and Loeschel, 2017]] ) is intimately related to the benefits of mitigation; a prerequisite is an assessment of how robustly magnitudes of warming can be defined ( [[#Millar--2017|Millar et al., 2017]] ). This chapter provides an update to the IPCC Special Report on Global Warming of 1.5°C (SR1.5, [[#IPCC--2018a|IPCC, 2018a]] ) and constitutes a reference point for later chapters and AR6 WGIII on the effects of mitigation, including a robust uncertainty assessment.

The chapter is organized as follows (Figure 4.1). After ( [[#4.2|Section 4.2]] on the methodologies used in the assessment, [[#4.3|Section 4.3]] assesses projected changes in key global climate indicators throughout the 21st century, relative to the period 1995–2014, which comprises the last 20 years of the historical simulations of CMIP6 ( [[#Eyring--2016|Eyring et al., 2016]] ) and hence the most recent past simulated with the observed atmospheric composition. The global climate indicators assessed include GSAT, global land precipitation, Arctic sea ice area (SIA), global mean sea level (GMSL), the Atlantic Meridional Overturning Circulation (AMOC), global mean ocean surface pH, carbon uptake by land and ocean, the global monsoon, the Northern and Southern Annular Modes (NAM and SAM), and the El Niño–Southern Oscillation (ENSO). Differently from the assessment for changes in other quantities only based on the range of CMIP6 projections, additional lines of evidence enter the assessment for GSAT and GMSL change. For most results and figures based on CMIP6, one realization from each model (the first of the uploaded set) is used. [[#4.3|Section 4.3]] finally synthesizes the assessment of GSAT change using multiple lines of evidence in addition to the CMIP6 projection simulations.

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[[File:bdabe60bddc5485c993f06604006e684 IPCC_AR6_WGI_Figure_4_1.png]]

'''Figure''' '''4.1 |''' '''Visual guide to Chapter 4.'''

( [[#4.4|Section 4.4]] covers near-term climate change, defined here as the period 2021–2040 and taken relative to the period 1995–2014. [[#4.4|Section 4.4]] focuses on global and large-scale climate indicators, including precipitation and circulation indices and selected modes of variability (see Cross-Chapter Box 2.2 and Annex IV: Modes of Variability), as well as on the spatial distribution of warming. The potential roles of short-lived climate forcers (SLCFs) and volcanic eruptions on near-term climate change are also discussed. [[#4.4|Section 4.4]] synthesizes information from initialized predictions and non-initialized projections for the near-term change.

( [[#4.5|Section 4.5]] then covers mid-term and long-term climate change, defined here as the periods 2041–2060 and 2081–2100, respectively, again relative to the period 1995–2014. The mid-term period is thus chosen as the twenty-year period following the short-term period and straddling the mid-century point, year 2050; it is during the mid-term that differences between scenarios are expected to emerge against internal variability. The long-term period is defined, as in AR5, as the 20-year period at the end of the century. [[#4.5|Section 4.5]] assesses the same set of indicators as ( [[#4.4|Section 4.4]] , as well as changes in internal variability and in large-scale patterns, both of which are expected to emerge in the mid- to long-term. The chapter sub-division according to time slices (near term, mid-term, and long term) is thus to a large extent motivated by the different roles that internal variability plays in each period, compared to the expected forced climate-change signal.

( [[#4.6|Section 4.6]] assesses the climate implications of climate policies, as simulated with climate models. First, [[#4.6|Section 4.6]] assesses patterns of climate change expected for various levels of GSAT rise including 1.5°C, 2°C, 3°C, and 4°C, compared to the approximation to the pre-industrial period 1850–1900 to facilitate immediate connection to SR1.5 and the temperature goals specified in the Paris Agreement ( [[#UNFCCC--2016|UNFCCC, 2016]] ). [[#4.6|Section 4.6]] continues with climate goals, overshoot, and path-dependence, as well as the climate response to mitigation, CDR, and SRM. [[#4.6|Section 4.6]] also covers the consistency between RCPs and SSPs.

( [[#4.7|Section 4.7]] assesses very long-term changes in selected global climate indicators, from 2100 to 2300. [[#4.7|Section 4.7]] continues with climate-change commitment and the potential for irreversibility and abrupt climate change. The chapter concludes with [[#4.8|Section 4.8]] on the potential for low-likelihood, high-impact storylines, followed by answers to three frequently asked questions (FAQs).

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