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

== 1.2 Different Entry Points for Understanding Climate Change Impacts, Adaptation and Vulnerability ==

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This section introduces key concepts used in this report and the connections between them that present different entry points for understanding climate change impacts, adaptation, and vulnerability.

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=== 1.2.1 Overlapping, Complementary Entry Points ===

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Many actors from different research and practice communities engage with understanding and responding to climate risk. Not surprisingly, there thus exist alternative, overlapping and complementary entry points to the discussion widely used throughout the literature and this report.

The concepts of risk and risk management have in recent years been central to climate change research and practice related to impacts, adaptation, and vulnerability. The concepts provide a framework for understanding climate change and its increasingly severe, interconnected and irreversible impacts. They support the implementation of solutions that reduce adverse consequences, pursue opportunities and enable beneficial outcomes for people, economies and nature ( [[#IPCC--2014c|IPCC, 2014c]] ; [[#IPCC--2018c|IPCC, 2018c]] ). All three AR6 Working Groups now apply a common risk framework ( [[#IPCC--2020|IPCC, 2020]] ).

Additional concepts—adaptation, vulnerability, exposure, resilience and transformation—also provide important framings for the climate change challenge.

Figure 1.2 displays the connections among many of the key concepts used in this report. This chapter, the Summary for Policymakers, Technical Summary and sectoral and regional chapters are organised around the concepts of risks (Section 1.3), solutions (Section 1.4) and transformation (Section 1.5).

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'''Figure 1.2 |''' '''Connecting key concepts in this report.''' The current coupled human and natural system is insufficiently resilient and does not meet societal goals of equity, well-being and ecosystem health. Meeting the objectives of the Paris Agreement, Sustainable Development Goals and other policy statements requires the system to move to a new and more resilient state. Key concepts used in this report help illuminate our current situation and potential solutions. These key concepts are usefully organised around the concepts of risk, solutions and transformation. Risk can prompt solutions and transformation. Both solutions and transformation seek to reduce some risks but may also generate others. Solutions can enable transformation, and transformation can expand the set of feasible solutions.

Key concepts that contribute to an understanding of risk include its components hazards, exposure, and vulnerability (Section 1.2.1.1); the recognition that risks may be complex and cascading (Section 1.3.1.2); and the reasons for concern framework used to summarise the most policy-relevant risks (Section 1.3.1.1). Key concepts that contribute to an understanding of feasible, effective, and just solutions (1.4.1) include the enablers of governance, finance and knowledge (Section 1.4.2); Transformation is supported by systems transitions in energy, land, infrastructure, industry and society (Section 1.5.1), which if successful can contribute to climate resilient development (Section 1.5).

The centre of Figure 1.2 shows societal goals of equity, health, well-being and climate justice as articulated by the Paris Agreement, SDGs, and other policies and plans (Section 1.4.1). The limits to adaptation (Section 1.4.4), potential for maladaptation (Sections 1.4.2; 17.5.2) and loss and damage (Section 1.4.4.2) present barriers to reaching these goals.

The concept of vulnerability can provide a unique window into the effects of climate change on different communities, individuals and ecosystems, in particular as human systems are affected by race, gender, wealth inequalities and other attributes (Section 1.2.1.2). The concept of adaptation can provide a unique window into the process of adjustment to climate change by human and natural systems (Section 1.2.1.3). Resilience (Section 1.3.1.4) is a broad concept, encompassing both outcomes and processes, an ability to maintain essential function and an ability to transform.

The ball and cup diagrams ( [[#Holling--1973|Holling, 1973]] ) in Figure 1.2 indicate that the current coupled human and natural system is not resilient, nor does it meet societal goals of equity, well-being and ecosystem health. Some types of transformation may prove inevitable (Section 1.5.1), either a deliberate transformation that results in a more resilient state consistent with societal goals or a forced transformation to a system state inconsistent with the goals.

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==== 1.2.1.1 Risk Framing ====

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'''Risk''' in this report is defined as the potential for adverse consequences for human or ecological systems, recognising the diversity of values and objectives associated with such systems. In the context of climate change impacts, risks result from dynamic interactions between climate-related hazards with the exposure and vulnerability of the affected human or ecological system. In the context of climate change responses, risks result from the potential for such responses not achieving the intended objective(s), or from potential trade-offs or negative side effects (see Annex II: Glossary). '''Risk management''' is defined as plans, actions, strategies or policies to reduce the likelihood and/or magnitude of adverse potential consequences, based on assessed or perceived risks (see Annex II: Glossary).

Risk framing is increasingly used to assess climate change impacts on human and natural systems (Sections 1.2.4.1; 16.1; 17.3; Cross-Chapter Box CLIMATE in Chapter 1; [[#IPCC--2012|IPCC, 2012]] ; [[#Mach--2017|Mach and Field, 2017]] ; [[#O’Neill--2017|]] [[#O’Neill--2017|O’Neill et al., 2017]] ; [[#Connelly--2018|Connelly et al., 2018]] ; see also [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ). A risk framing reflects key dimensions of the climate challenge. These features include the changing likelihoods of many different outcomes (including adverse consequences and beneficial opportunities), uncertainties that will persist, and different and contested values, priorities and goals ( [[#Jones--2011|Jones and Preston, 2011]] ; [[#Mach--2016|Mach et al., 2016]] ). The IPCC AR6 and associated special reports apply a broad definition of risk. WGI Cross-Chapter Box 1.3. WGI uses the Climatic Impact Driver terminology, rather than hazard, to neutrally assess changing climatic conditions that are relevant to human and natural systems, leaving the determination of positive/negative consequences and resulting impacts and risks for WGII assessment (WGI Section 12.3). In most cases, throughout this WGII report, the term ‘risk’ refers to the risks of climate change impacts. The full assessment, however, incorporates all relevant risks from climate change impacts and responses.

The broad definition of risk involves quantitative and integrative understandings of risk ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ; [[#Mach--2017|Mach and Field, 2017]] ; see also Section 17.3). Risk is sometimes defined as the probability of a consequence, multiplied by the magnitude of that consequence, acknowledging both the diversity of possible consequences and the relevance of values. Yet it also applies in circumstances where probabilities cannot be fully quantified (e.g., [[#Adger--2013|Adger et al., 2013]] ). For example, in some cases the probability and magnitude of consequences may be more uncertain, dependent on complex dimensions of the climate (e.g., a cyclone, high tide, and heatwave co-occurring) or the vulnerability of different communities (e.g., the ways in which social networks and community cohesion support the most vulnerable individuals during disasters) ( [[#Ford--2018|Ford et al., 2018]] ). The determinants of risk vary dynamically through space and time ( [[#Jurgilevich--2017|Jurgilevich et al., 2017]] ; [[#Viner--2020|Viner et al., 2020]] ). They interact, compound, and cascade ( [[#Dawson--2015|Dawson, 2015]] ; Adger et al., 2018; see also Section 16.1.2).

Risk framing supports connections with solutions ( [[#Jones--2011|Jones and Preston, 2011]] ; [[#Mach--2016|Mach et al., 2016]] ; Adger et al., 2018). First, risk framing connects the present with the future. (Papathoma-Kohle et al., 2016). For instance, whether wildfire or drought, recent experiences have demonstrated limits to current response capacities relevant to future preparedness (e.g., evacuation of large communities on tight time frames or water management simultaneously responsive to intensifying drought and flooding). Second, risk framing emphasises that uncertainties and complex interactions are integral to decision making ( [[#Jones--2014|Jones et al., 2014]] ; [[#Dawson--2015|Dawson, 2015]] ). The uncertainties include high-impact, low-probability outcomes and deep uncertainties for which core processes are not understood and meaningful probabilities cannot be applied ( [[#Adler--2016|Adler et al., 2016]] ; see also Section 17.2.1; Cross-Chapter Box DEEP in Chapter 17; [[IPCC:Wg2:Chapter:Chapter-7|Chapter 7]] in SRCCL, [[#IPCC--2019a|IPCC, 2019a]] ; Cross-Chapter Box 5 in SROCC, [[#IPCC--2019b|IPCC, 2019b]] ). In these circumstances, risk assessment can occur through tools used for risk management across contexts, such as insurance, business, social protection, security and policy planning, and decision making can be iterative and support dynamic adaptive pathways through time ( [[#Jones--2011|Jones and Preston, 2011]] ; [[#Watkiss--2015|Watkiss et al., 2015]] ; [[#Aven--2016|Aven, 2016]] ; see also Section 17.3.2)

'''Iterative risk management''' ( [[#Vervoort--2018|Vervoort and Gupta, 2018]] ) emphasises that anticipating and responding to climate change does not consist of a single set of judgements at a single point in time, but rather an ‘ongoing cycle of assessment, action, reassessment, learning and response ( [[#USGCRP--2018|USGCRP, 2018]] ). It is consistent with most approaches applied for implementing adaptation ( [[#Jones--2011|Jones and Preston, 2011]] ; [[#Jones--2014|Jones et al., 2014]] ). For instance, the Paris Agreement is organised as a polycentric process (see Section 1.4) of iterative risk management in which national governments pledge to take specific actions. Those actions are monitored and assessed, and nations asked to update their pledges in light of that assessment.

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==== 1.2.1.2 Vulnerability ====

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'''Vulnerability''' is a component of risk, but also an important focus independently. Vulnerability in this report is defined as the propensity or predisposition to be adversely affected. It encompasses a variety of concepts and elements, including sensitivity or susceptibility to harm and lack of capacity to cope and adapt (see Annex II: Glossary). Over the past several decades, approaches to analysing and assessing vulnerability have evolved. An early emphasis on top-down, biophysical evaluation of vulnerability included—and often started with—exposure to climate hazards in assessing vulnerability. From this starting point, attention to bottom-up, social and contextual determinants of vulnerability, which often differ, has emerged, although this approach is incompletely applied or integrated across contexts ( [[#Bergstrand--2015|Bergstrand et al., 2015]] ; [[#Rufat--2015|Rufat et al., 2015]] ; [[#Spielman--2020|Spielman et al., 2020]] ; [[#Taberna--2020|Taberna et al., 2020]] ). Vulnerability is now widely understood to differ within communities and across societies, also changing through time (Kienberger et al., 2013; [[#Jurgilevich--2017|Jurgilevich et al., 2017]] ; see also Chapter 16). In the WGII AR6, assessment of the vulnerability of people and ecosystems encompasses the differing approaches that exist within the literature, both critiquing and harmonising them based on available evidence. In this context, '''exposure''' is defined as the presence of people; livelihoods; species or ecosystems; environmental functions, services, and resources; infrastructure; or economic, social, or cultural assets in places and settings that could be adversely affected (Annex II: Glossary). Potentially affected places and settings can be defined geographically, as well as more dynamically, for example through transmission or interconnections through markets or flows of people.

Vulnerability is also a link between the climate risk and disaster risk communities, recognising complementarities and differences between these communities. '''Disaster risk management''' is the set of processes that improve understanding of disaster risk, foster disaster risk reduction and transfer, and promote continuous improvement in disaster preparedness, response and recovery practices, increasing human security, well-being and sustainable development (see Annex II: Glossary). Climate risk and disaster risk are increasingly addressed together, bridging the climate change adaptation and disaster risk reduction communities (e.g., [[#IPCC--2012|IPCC, 2012]] ; [[#UNDRR--2019|UNDRR, 2019]] , especially Chapter 13 in that report). Building on the scientific literature and adaptation and risk reduction practice, the IPCC Special Report on Extremes resulted in several major IPCC advances that continue through the present report, including emphasis on risk and climate-related extremes (e.g., [[#Burton--2012|Burton et al., 2012]] ; [[#Lavell--2012|Lavell et al., 2012]] ) and re-conceptualisation of vulnerability to encompass both social and biophysical orientations (i.e., bridging contextual/bottom-up and climate-driven/top-down approaches) ( [[#polsky--2007|Polsky et al., 2007]] ; [[#Cardona--2012|Cardona et al., 2012]] ). Linking disaster risk reduction and climate change adaptation can also be an important basis for discussion in climate negotiations on the allocation of funds needed for tackling climate change, especially in developing countries and SIDS ( [[#Begum--2014|Begum et al., 2014]] ). The integration of disaster risk management and climate change adaptation in the IPCC AR6 is seen, for example, in the assessment of key risks within and across sectors and regions, along with global-scale reasons for concern, which is attuned to extreme events and disasters ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ; see also Chapter 16). Additionally, the assessment of adaptation has prioritised these interconnections (e.g., [[#Mimura--2014|Mimura et al., 2014]] ), as have literature and practice especially in the context of sustainable development (e.g., [[#Schipper--2016|Schipper et al., 2016]] ).

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==== 1.2.1.3 Adaptation ====

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'''Adaptation''' in this report is defined, in human systems, as the process of adjustment to actual or expected climate and its effects, in order to moderate harm or exploit beneficial opportunities. In natural systems, adaptation is the process of adjustment to actual climate and its effects; human intervention may facilitate adjustment to expected climate and its effects (see Annex II: Glossary). Adaptation planning in human systems generally entails a process of iterative risk management. Different types of adaptation have been distinguished, including anticipatory versus reactive, autonomous versus planned and incremental versus transformational adaptation (Chapters 16–18; IPCC WGII glossaries for the TAR, AR4, AR5, and AR6 (Annex II)). Adaptation is often seen as having five general stages: (a) awareness, (b) assessment, (c) planning, (d) implementation and (e) M&amp;E ( [[#Moser--2013|Moser and Boykoff, 2013]] ; [[#Jones--2014|Jones et al., 2014]] ; [[#Mimura--2014|Mimura et al., 2014]] ; [[#Noble--2014|Noble et al., 2014]] ; see also Section 17.4). Government, non-government and private sector actors have adopted a wide variety of specific approaches to adaptation that, to varying degrees, address these five general stages. Adaptation in natural systems includes ‘autonomous’ adjustments through ecological and evolutionary processes. It also involves the use of nature through ecosystem-based adaptation. The role of species, biodiversity and ecosystems in such adaptation options can range from the rehabilitation or restoration of ecosystems (e.g., wetlands or mangroves) to hybrid combinations of ‘green and grey’ infrastructure (e.g., horizontal levees) (Chapters 2 and 3; [[#IPBES--2018|IPBES, 2018]] ).

The IPCC assessment of adaptation has evolved through time. The WGII AR4 included one chapter dedicated to adaptation, the WGII AR5 expanded to four and the WGII AR6 mainstreams adaptation comprehensively throughout the report. Adaptation science is rapidly evolving, including evaluation of adaptation effectiveness, feasibility, implementation and maladaptation, although major knowledge gaps persist in modelling and analysis (Cross-Chapter Box ADAPT in Chapter 1; Chapter 16; Section 1.4; [[#Holman--2019|Holman et al., 2019]] ). The WGII AR6 emphasises assessment of observed adaptation-related responses to climate change, governance and decision making in adaptation, and the role of adaptation in reducing key risks and global-scale reasons for concern, as well as limits to such adaptation (e.g., Chapters 16 and 17). The assessment approach includes adaptation needs, options, planning and implementation across sectors and regions, as well as adaptation opportunities, constraints and also limits ( [[#Eisenack--2014|Eisenack et al., 2014]] ; [[#Klein--2014b|Klein et al., 2014b]] ; [[#Oberlack--2014|Oberlack and Eisenack, 2014]] ; [[#Lehmann--2015|Lehmann et al., 2015]] ; [[#Roggero--2015|Roggero, 2015]] ; [[#Herrmann--2017|Herrmann and Guenther, 2017]] ; [[#Oberlack--2017|Oberlack, 2017]] ; Sieber et al., 2018; [[#Moser--2019b|Moser et al., 2019b]] ; [[#Capela%20Lourenço--2019|Capela Lourenço et al., 2019]] ; [[#Thaler--2019|Thaler et al., 2019]] ; [[#Russel--2020|Russel et al., 2020]] ; see also Chapters 16 and 17).

Since AR5, more adaptation has progressed ( [[#IPCC--2014a|IPCC, 2014a]] ; [[#Lesnikowski--2016|Lesnikowski et al., 2016]] ; see also Sections 16.2.5 and 17.2) and the focus of activity has expanded to include social, institutional and governance dimensions beyond engineered and technical options and to decision processes beyond technocratic, linear framings ( [[#IPCC--2014a|IPCC, 2014a]] ; see also Chapter 17). Adaptation includes increasing attention to implementation, M&amp;E and learning through time, not just planning processes (Section 17.3 and 17.5.1). On the one hand, an important advance has been recognition of generalised capacities, such as resources and knowledge, necessary for the feasibility of effective adaptation. Adaptation thereby strongly overlaps with risk management and with the building of resilience and sustainable development (Chapters 17 and 18).

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==== 1.2.1.4 Resilience, Including Connections with Development Pathways and Transformation ====

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'''Resilience''' in this report is defined as the capacity of social, economic and environmental systems to cope with a hazardous event or trend or disturbance, responding or reorganising in ways that maintain their essential function, identity and structure, while also maintaining the capacity for adaptation, learning and transformation (see Annex II: Glossary). Resilience is an entry point commonly used, although under a wide spectrum of meanings ( [[#Reghezza-Zitt--2012|Reghezza-Zitt et al., 2012]] ; [[#Flood--2014|Flood and Schechtman, 2014]] ; [[#Aldunce--2015|Aldunce et al., 2015]] ; [[#Tanner--2015|Tanner et al., 2015]] ; Fisichelli et al., 2016; Meerow et al., 2016; [[#Moser--2019a|Moser et al., 2019a]] ). Resilience as a system trait overlaps with concepts of vulnerability, adaptive capacity, and thereby risk. Resilience as a strategy overlaps with risk management, adaptation and also transformation ( [[#Woodruff--2018|Woodruff et al., 2018]] ; [[#Moser--2019a|Moser et al., 2019a]] ). Implemented adaptation is often organised around resilience as bouncing back and returning to a previous state after a disturbance (Fisichelli et al., 2016).

In much of the literature, resilience encompasses not just maintaining essential function, identity and structure, but also maintaining a capacity for adaptation, learning and transformation. Since the earliest framings of resilience around stability and persistence, ecology and allied fields have come to recognise that while systems are often persistent in the face of disturbance, disturbance also creates opportunity for transformation and the emergence of new pathways (Section 1.5.2; [[#Folke--2006|Folke, 2006]] ; [[#Allen--2010|Allen and Holling, 2010]] ; [[#Folke--2010|Folke et al., 2010]] ; [[#Gelcich--2010|Gelcich et al., 2010]] ; [[#Stockholm%20Resilience%20Center--2015|Stockholm Resilience Center, 2015]] ; [[#Doppelt--2017|Doppelt, 2017]] ). Across this literature, disturbance is framed as outside the system in question, for which the time frames and spatial scales of disturbances, impacts and responses are central to outcomes ( [[#Béné--2011|Béné et al., 2011]] ; [[#Brown--2014|Brown, 2014]] ; [[#Hamborg--2020|Hamborg et al., 2020]] ). Endogenous processes of transformation are presented as emergent, characterised by thresholds and, as a result, very difficult to anticipate ( [[#Scheffer--2001|Scheffer et al., 2001]] ; [[#Walker--2004|Walker and Meyers, 2004]] ; [[#Suding--2009|Suding and Hobbs, 2009]] ; [[#Scheffer--2012|Scheffer et al., 2012]] ; [[#Hughes--2013|Hughes et al., 2013]] ; [[#Scheffer--2015|Scheffer et al., 2015]] ). In the last 5 years (2016–2020), the concept of resilience has gained prominence as a core theme in the climate change adaptation literature ( [[#Nalau--2021|Nalau and Verrall, 2021]] ).

Often, development and adaptation communities of practice default to persistence and stability in their use of resilience ( [[#Cote--2012|Cote and Nightingale, 2012]] ; [[#MacKinnon--2013|MacKinnon and Derickson, 2013]] ). Such a framing aligns resilience with a long-standing but increasingly questioned belief that sustainable development can be achieved through incremental adjustments in behaviour and advances in technology that allow for the persistence of existing socioeconomic and socio-ecological arrangements ( [[#Klauer--1999|Klauer, 1999]] ; [[#Banerjee--2003|Banerjee, 2003]] ; [[#Redclift--2005|Redclift, 2005]] ; UN Inter-agency Task Force on Financing for Development 2019; Chapter 18, Section 1.5). However, the literature increasingly suggests that the achievement of sustainable development will require transformative change in socio-ecological systems at scales ranging from the community to the globe. The concept of climate resilient development, initially introduced in AR5 and now a key focus in this report (see Chapter 18), engages with such transformations and the associated questions of justice, power and politics as shaped by internal, endogenous social factors and their interactions with other drivers of change (Eriksen et al., 2015; [[#Nightingale--2015b|Nightingale, 2015b]] ; [[#Carr--2019|Carr, 2019]] ; [[#Nightingale--2019|Nightingale et al., 2019]] ; see also Chapter 18).

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'''Cross-Chapter Box CLIMATE | Climate Reference Periods, Global Warming Levels and Common Climate Dimensions'''
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Authors: Steven Rose (USA), Richard Betts (UK), Philippus Wester (Nepal/the Netherlands), Aris Koutroulis (Greece)

This Cross-Chapter Box sets out common climate dimensions to contextualise and facilitate AR6 WGII analyses, presentation, synthesis and communication of assessed, observed and projected climate change impacts across WGII chapters and cross-chapter papers. ‘Common climate dimensions’ are defined as common global warming levels (GWLs), time periods and levels of other variables, as needed by WGII authors for consistent communications. The set of climate variable ranges given below was derived from the AR6 WGI report and supporting resources, and helps to contextualise and inform the projection of potential future climate impacts and key risks. The information enables the mapping of climate variable levels to climate projections and vice versa, with ranges of results provided to characterise the physical uncertainties relevant to assessing climate impacts risk.

AR6 WGI Reference Periods, Climate Projections and Global Warming Levels

AR6 WGI adopts a common set of reference years and time periods to assess observed and projected climate change, namely the pre-industrial period, the current ‘modern’ period and future reference time periods. The IPCC Glossary (2021b) defines the pre-industrial period as ‘the multi-century period prior to the onset of large-scale industrial activity around 1750. The reference period 1850–1900 is used to approximate pre-industrial global mean surface temperature (GMST).’ The ‘modern’ period is defined as 1995 to 2014 in AR6, while three future reference periods are used for presenting climate change projections, namely near term (2021–2040), mid-term (2041–2060) and long term (2081–2100), in both the AR6 WGI and WGII reports. Importantly, the historical rate of warming assessed by WGI in AR6 is different to that assessed in AR5 and Special Report on Global Warming of 1.5°C (SR1.5, [[#IPCC--2018b|IPCC, 2018b]] ), due to methodological updates (see WGI Cross-Chapter Box 2.3 in [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] for details (Gulev, 2021)). This means that the ‘modern’ period is assessed as slightly warmer compared to 1850–1900 than it would have been with AR5-era methods. This also has implications for the projected timing of reaching policy-relevant levels of global warming, which need to be understood.

To explore and investigate climate futures, climate change projections are developed using sets of different input projections. These consist of sets of projections of GHG emissions, aerosols or aerosol precursor emissions, land use change, and concentrations designed to facilitate evaluation of a large climate space and enable climate modelling experiments. For AR5 (and the Coupled Model Intercomparison Project (CMIP) 5 climate model experiments), the input projections were referred to as representative concentration pathways (RCPs). For AR6 (and the CMIP6 climate model experiments), new sets of inputs are used and referred to as SSP scenarios, where SSP refers to socioeconomic assumptions called the shared socioeconomic pathways (SSPs).

The RCPs are a set of four trajectories that span a large radiative forcing range, defined as increased energy input at surface level in Watts per square metre, ranging from 2.6 W m -2 (RCP2.6) to 8.5 W m -2 (RCP8.5) by the end of the 21st century, with RCP4.5 and RCP6.0 as intermediate scenarios, and RCP2.6 a peak and decline scenario reaching 3 W m -2 before 2100. A range of emissions scenarios compatible with each specific RCP was also assessed in AR5 ( [[#Ciais--2013|Ciais et al., 2013]] ).

A core set of five SSP scenarios, namely SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, was selected in the AR6 WGI report to fill certain gaps identified in the RCPs (see WGI Cross-Chapter Box 1.4 in [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-1 Chapter 1] ( [[#Chen--2021|Chen et al., 2021]] )). The first number in the label is the particular set of socioeconomic assumptions driving the emissions and other climate forcing inputs taken up by climate models and the second number is the radiative forcing level reached in 2100. WG1 Cross-Chapter Box 1.4 in [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-1 Chapter 1] provides a comparison of this core set of SSP scenarios with scenarios used in previous reports, with SSP1–1.9 a low overshoot scenario consistent with limiting global average warming to 1.5°C, and SSP1-2.6 a scenario consistent with limiting warming to 2°C.

Also of importance to the impact literature and the WGII report are SSP-RCP combinations, that is, studies that employ climate outcomes based on RCPs and socio-economic assumptions based on SSPs. SSPs can be paired with a range of different RCPs because SSPs can be combined with mitigation policy assumptions to produce a range of emissions pathways. In addition to the SSPs, there are many other emissions pathways and societies consistent with any global mean temperature outcome. These represent uncertainty and broad ranges of possibilities that affect climate change exposure and vulnerability (Rose and and M. Scott, 2020; [[#Rose--2018|Rose and Scott, 2018]] ). Furthermore, there are large uncertainties in translating emissions scenarios into concentration pathways due to uncertainties in climate-carbon cycle feedbacks ( [[#Jones--2013|Jones et al., 2013]] ; [[#Booth--2017|Booth et al., 2017]] ).

The plausibility of emissions levels as high as the emissions scenario conventionally associated with the RCP8.5 and SSP5-8.5 concentration pathways has been called into question since AR5, as has the emissions pathway feasibility of the low scenarios ( [[#Hausfather--2020|Hausfather and Peters, 2020]] ; Rose and and Scott, 2020). However, these views are contested (Schwalm et al., 2020, for RCP8.5) It is important to realise that emissions scenarios and concentration pathways are not the same thing and higher concentration pathways, such as RCP8.5 could arise from lower emissions scenarios if carbon cycle feedbacks are stronger than assumed in the integrated assessment models (IAMs) used to create the standard scenarios ( [[#Booth--2017|Booth et al., 2017]] ). In the majority of full-complexity Earth System Models, these feedbacks are stronger than in the IAMs ( [[#Jones--2013|Jones et al., 2013]] ), so the RCP8.5 concentration pathway cannot be ruled out purely through consideration of the economic aspects of emissions scenarios. Nonetheless, the likelihood of a climate outcome, and the overall distribution of climate outcomes, are a function of the emissions scenario’s likelihood. Note that the original RCPs were created explicitly to facilitate a broad range of climate modelling experiments, with the expectation that other issues, such as socioeconomic uncertainty, could be subsequently explored ( [[#Moss--2010|Moss et al., 2010]] ).

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Cross-Chapter Box CLIMATE1

An important feature of the AR6 cycle is a stronger emphasis on the use of future GWLs to support consistency and comparability across the three IPCC Working Groups’ contributions to the AR6 and improve communication. The common range of GWLs relative to the 1850 to 1900 period, termed the ‘Tier 1’ range by WGI, are 1.5, 2.0, 3.0 and 4.0°C. The use of GWLs assists in the comparison of climate states across climate change scenarios (projections) and in assessing the broader literature, as well as for cross-chapter and cross-working group comparisons. They facilitate the integration of climate projections, impacts, adaptation challenges and mitigation challenges within and across the three Working Groups as there is a close connection between the level of global warming and climate change impacts. Of particular interest is the timing of when the ‘Tier 1’ GWLs are reached, relative to the period 1850–1900, under the five SSP x–y scenarios, as well as RCP scenarios. For climate change impacts and adaptation responses, linking GWLs to RCP and SSP climate projections using a climate information translation resource is of great relevance for the WGII contribution to AR6.

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Cross-Chapter Box CLIMATE1

AR6 WGII Common Climate Dimensions

WGII’s common climate dimensions include (a) a common range of GWLs from WGI, (b) common ranges for other climate variables, (c) information for translating climate variable levels to climate projections and vice versa. See Table Cross-Chapter Box CLIMATE.1 for global warming level ranges by time periods for RCP and SSP climate projections, and Table Cross-Chapter Box CLIMATE.2 for information regarding the timing for when GWLs are reached in climate projections. The common GWL range is based on WGI’s ‘Tier 1’ dimensions of integration range: 1.5°C, 2°C, 3°C and 4°C. The first table illustrates the greater levels of projected global warming with higher emissions pathways, as well as the increasing uncertainty in the climate response over time for a given pathway. The second table illustrates significant uncertainty in the timing for passing GWL thresholds which can narrow for a given GWL, the higher the emissions pathway. Finally, given the importance of geographic heterogeneity in projected changes in future climate, Table Cross-Chapter Box CLIMATE.3a and 3b are provided with ranges for select climate variables (temperature, precipitation, ocean) by GWL and continent (or ocean biome). The ranges illustrate spatial heterogeneity in potential physical changes in levels and uncertainty that are relevant to assessing climate impacts risk. There is significantly more spatial heterogeneity than represented in the table that is relevant to local decision makers (see, for instance, WGI Interactive Atlas).

The common climate dimensions can be used as a dimension of integration for impact studies in WGII, for example by providing a common framework for comparison of projected impacts for different studies (Figure Cross-Chapter Box CLIMATE.1). Moreover, GWL bands are needed in WGII to map the diverse temperature levels found across WGII’s literature. The GWL’s also facilitate integration with WGIII’s global emissions projections categorisation by global mean temperature (WGIII Chapter 3).

'''Table Cross-Chapter Box CLIMATE.1 |''' GWL ranges by time periods for CMIP5 (RCP) and CMIP6 (SSP) climate projections (20-year averages). Temperature anomalies relative to 1850–1900. Full ranges for CMIP raw results (across all models and ensemble runs) and WGI AR6 assessed ''very likely'' (5–95%) ranges. ''Sources: [[#hauser--2019|Hauser et al. (2019)]] ; WGI SPM ( [[#IPCC--2021a|IPCC, 2021a]] ); Table SPM.1''

{| class="wikitable"
|-
!
! colspan="9"| '''Full ranges'''

! colspan="9"| '''WGI AR6 assessed''' '''''very likely''''' '''(5–95%) ranges'''

|-
! '''Projection'''

! colspan="3"| '''2021–2040'''

! colspan="3"| '''2041–2060'''

! colspan="3"| '''2081–2100'''

! colspan="3"| '''2021–2040'''

! colspan="3"| '''2041–2060'''

! colspan="3"| '''2081–2100'''

|-
| RCP2.6

| 1.0

| to

| 2.2

| 1.0

| to

| 2.3

| 0.9

| to

| 2.3

| colspan="3"| n/a

| colspan="3"| n/a

| colspan="3"| n/a

|-
| RCP4.5

| 1.1

| to

| 2.2

| 1.4

| to

| 2.7

| 1.8

| to

| 3.3

| colspan="3"| n/a

| colspan="3"| n/a

| colspan="3"| n/a

|-
| RCP6.0

| 1.0

| to

| 2.0

| 1.3

| to

| 2.5

| 2.3

| to

| 3.6

| colspan="3"| n/a

| colspan="3"| n/a

| colspan="3"| n/a

|-
| RCP8.5

| 1.1

| to

| 2.6

| 1.7

| to

| 3.7

| 3.0

| to

| 6.2

| colspan="3"| n/a

| colspan="3"| n/a

| colspan="3"| n/a

|-
| SSP1–1.9

| 1.0

| to

| 2.4

| 1.1

| to

| 2.7

| 1.0

| to

| 2.5

| 1.2

| to

| 1.7

| 1.2

| to

| 2.0

| 1.0

| to

| 1.8

|-
| SSP1–2.6

| 1.0

| to

| 2.4

| 1.2

| to

| 2.9

| 1.3

| to

| 3.1

| 1.2

| to

| 1.8

| 1.3

| to

| 2.2

| 1.3

| to

| 2.4

|-
| SSP2–4.5

| 0.9

| to

| 2.5

| 1.3

| to

| 3.3

| 1.9

| to

| 4.4

| 1.2

| to

| 1.8

| 1.6

| to

| 2.5

| 2.1

| to

| 3.5

|-
| SSP3–7.0

| 1.0

| to

| 2.6

| 1.5

| to

| 3.7

| 2.7

| to

| 6.2

| 1.2

| to

| 1.8

| 1.7

| to

| 2.6

| 2.8

| to

| 4.6

|-
| SSP5–8.5

| 1.0

| to

| 2.7

| 1.6

| to

| 4.0

| 3.1

| to

| 7.2

| 1.3

| to

| 1.9

| 1.9

| to

| 3.0

| 3.3

| to

| 5.7

|}

'''Table Cross-Chapter Box CLIMATE.2 |''' '''Timing for when 20-year average GWLs are reached in CMIP5 (RCP) and CMIP6 (SSP) climate projections.''' GWL anomalies relative to 1850–1900. Ranges based on CMIP raw results (all models and ensemble runs), and WGI AR6 assessed results. For each GWL and RCP/SSP, the earliest and latest 20-year window when a 20-year average GWL is reached across the CMIP models and ensemble members is reported, or the ''very likely'' (5–95%) assessed range is reported. ‘n.c.’ means the GWL is not reached during the period 2021–2100. ''Sources: Hauser et al. (2019); WGI TS Cross-Section Box Table TS.1( [[#Arias--2021|Arias et al., 2021]] )''

[[File:9c8a83a119a2d5609fddb4d8e19beacb IPCC_AR6_WGII_Chapter1_Table_CCBOX_Climate2.png]]
'''Table Cross-Chapter Box CLIMATE.3a |''' '''Projected continental level result ranges for select temperature and precipitation climate change variables by global warming level.''' Ranges are 5 th and 95 th percentiles from SSP5–8.5 WGI CMIP6 ensemble results. There is little variation in the 5 th and 95 th percentile values by GWL across the SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 projections. ''Source: WGI AR6 Interactive [https://www.ipcc.ch/chapter/atlas Atlas] ('' https://interactive-atlas.ipcc.Chapter/ '').''

[[File:fc9c7bb4af92e457044093883e993160 IPCC_AR6_WGII_Chapter1_Table_CCBOX_Climate3a.png]] [[File:80a4d420e4587568d68f4d4e70cd3eab IPCC_AR6_WGII_Chapter1_Table_CCBOX_Climate3a2.png]]
'''Table Cross-Chapter Box CLIMATE.3b |''' Projected sea surface temperature change ranges by global warming level and ocean biome (degrees Celsius). Ranges are 5 th and 95 th percentiles from SSP5–8.5 WGI CMIP6 ensemble results. There is little variation in the 5 th and 95 th percentile values by GWL across the SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 projections. ''Source: WGI Interactive [https://www.ipcc.ch/chapter/atlas Atlas] ('' https://interactive-atlas.ipcc.Chapter/ '').''

[[File:13a9e86c2ed8802c6730a4a30d9d7812 IPCC_AR6_WGII_Chapter1_Table_CCBOX_Climate3b.png]]

[[File:82c391a6ad48701ee389482d8a417718 IPCC_AR6_WGII_Figure_1_Cross-Chapter_Box_CLIMATE_1.png]]

'''Figure Cross-Chapter Box CLIMATE.1 |''' '''Illustration of the use of global warming levels (GWLs) as a dimension of integration for impact studies: projected changes in river flows in major basins at 4°C global warming from four different multi-model ensembles.''' Results are shown for projected flow changes direct from Earth System Models (ESMs) in CMIP5 and CMIP6, for the Joint UK Land Environment Simulator (JULES) land surface model driven by meteorological outputs of the HadGEM3 and EC-Earth model in the High-End cLimate Impacts and eXtremes (HELIX) ensemble (Betts et al., 2018; Koutroulis et al., 2019), and nine hydrological models driven by a subset of five CMIP5 ESMs in the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP; Warszawski et al., 2014). Dots show results from individual models, blue for increased flows and red for decreased flows, black circles show the median for each ensemble, and black bars show the 95% confidence range in the median. See Figure 4.11 for further details.

To contextualise reported impacts by warming level for the influence of other determinants of risk, where appropriate and feasible (e.g., level of exposure/vulnerability, level of adaptation, time period), common time periods for the past and future can be aligned with WGI’s historical and projected time windows. Given differences in available literature, WGII chapters and CCPs (cross-chapter papers) contextualise impacts with respect to exposure, vulnerability and adaptation as appropriate.

Common ranges for other ‘climate’ variables, such as minimum and maximum temperatures and regional climates, are available based on WGI projections. They are based on feasible combinations with GWLs taken into consideration using the WGI Interactive Atlas. Climate information translation may have been necessary within chapters for mapping the WGII literature and assessments of the common climate dimensions. WGII’s climate impacts literature is based primarily on climate projections around AR5 and earlier or assumed temperature levels, though some recent impacts literature uses newer climate projections based on the CMIP6 exercise. Thus, it was important to be able to map climate variable levels to climate projections of different vintages and vice versa and adjust variables, when possible, to a common reference year. WGII chapters and CCPs only provide climate impact information for the common climate dimensions that their literature supports and where there is sufficient evidence.

Interpretation of the update in projected time of reaching 1.5°C global warming from SR1.5 to AR6

In an assessment using multiple lines of evidence, including models, observational constraints and improved understanding of climate sensitivity, WGI project a central estimate of the 20-year average warming crossing the 1.5°C GWL in the early 2030s in all scenarios assessed, except SSP5–8.5 ( [[#Lee--2021|Lee et al., 2021]] ). This is about 10 years earlier than the midpoint of the likely range (2030–2052) assessed in SR1.5, which assumed continuation of the observed warming rate reported at that time. However, this does not imply that the projected impacts of 1.5°C will be reached 10 years earlier, because roughly half of the 10-year difference is a result of updating the diagnosed historical rate of warming due to methodological advances, new datasets and other improvements ( [[#Gulev--2021|Gulev et al., 2021]] ). The other half of the 10-year difference arises because, for central estimates of climate sensitivity, most scenarios show stronger warming over the near term than was assessed as ‘current’ in SR1.5 ( ''medium confidence'' ).

The revised historical warming rate does not necessarily contribute to a change in timing of estimated impacts. It depends on how impacts are calculated relative to climate. Because the revised historical warming results in a redefinition of the 1.5°C GWL relative to the modern time period (1995–2014) rather than a different level of overall change (Figure Cross-Chapter Box CLIMATE.2 in Chapter 1), impacts assessed relative to the modern time period are unaffected. There are, in effect ‘old’ and ‘new’ definitions of the 1.5°C GWL with different levels of impacts, and the impacts assessed for the ‘old’ 1.5°C GWL now apply to a different level of global warming. However, the timing of impacts assessed relative to pre-industrial (e.g., aggregate economic impact estimates), are affected and we are closer to impact levels associated with 1.5°C and 2°C.

To illustrate with a worked example: in SR1.5, the historical warming between 1850–1900 and the modern period of 2006–2015 was assessed as 0.87°C, implying that the 1.5°C GWL would be accompanied by impacts associated with 0.63°C warming from the modern period. However, AR6 WGI ( [[#Gulev--2021|Gulev et al., 2021]] ) revised the assessment of warming between 1850–1900 and 2006–2015 to 0.94°C, implying that the 1.5°C GWL would be accompanied by a slightly lower level of impacts associated with only 0.56°C warming from the modern period. So, while the redefined 1.5°C GWL would be reached earlier, it would also be accompanied by a lower level of impacts (Figure Cross-Chapter Box CLIMATE.2 in Chapter 1). The impacts associated with the ‘old’ 1.5°C GWL would now be seen at 1.57°C global warming relative to 1850–1900, reached at the time of the ‘old’ 1.5°C GWL, if the same future level of warming were to be used as in SR1.5.

However, in addition to this redefinition of the historical warming rate, the assessed future warming in AR6 is also slightly faster than the continuation of reported recent warming used in SR1.5. This means that both the ‘old’ and ‘new’ 1.5°C GWLs are projected to be reached earlier than they would have been using the SR1.5 method. This and the revised historical warming diagnosis contribute approximately equally to the assessment of 1.5°C global warming being reached about 10 years earlier than projected in SR1.5.

Central estimates of impacts associated with a specifically defined 1.5°C GWL could therefore be considered to be projected to be reached approximately 5 years earlier than implied by SR1.5. However, uncertainties in regional climate responses at a given GWL are large (Cross-Chapter Box CLIMATE in Chapter 1, Table CLIMATE.3a) and natural climate variability occurs in parallel with ongoing warming, so the potential for impacts higher than central estimates could be a more urgent consideration for risk assessments and adaptation planning than the earlier projected timing of reaching 1.5°C ( ''high confidence'' ). It should also be noted that individual years may exceed 1.5°C above 1850–1900 sooner, but this is not the same as exceedance of the 1.5°C GWL which refers to the 20-year mean.

[[File:7e43102ea0d85ece597280d81e4b5c1c IPCC_AR6_WGII_Figure_1_Cross-Chapter_Box_CLIMATE_2.png]]

'''Figure Cross-Chapter Box CLIMATE.2 |''' '''Definitions of the 1''' '''.''' '''5°C global warming level (GWL) in SR1.5 ( [[#IPCC--2018b|IPCC, 2018b]] ) and AR6 WGI ( [[#IPCC--2021a|IPCC, 2021a]] ).''' GWLs are defined relative to 1850–1900 but impacts at the GWL are typically assessed in association with warming relative to a modern period 1995–2014, which in SR1.5 was 2006–2015. Revised assessment of the historical warming between 1850–1900 and the modern period (0.87°C in SR1.5 to 0.94°C in AR6) has the effect of slightly reducing the warming between the modern period and the 1.5°C GWL (0.63°C in SR1.5 to 0.56°C in AR6), and the impacts at the GWL previously defined as 1.5°C in SR1.5 now occur at 1.57°C global warming with the AR6 definition. Warming values are central estimates. Heights of the bars are not to scale.

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=== 1.2.2 Narratives, Storylines, Scenarios and Pathways ===

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The concepts of narratives, storylines, scenarios and pathways play an important role in this report. While distinct concepts, they are inter-related and sometimes confused.

A '''narrative''' is a story with a chronological order or, when cast in the form of an argument, with premises and conclusions ( [[#Roe--1991|Roe, 1991]] ; [[#Adger--2001|Adger et al., 2001]] ). Narratives enable people to envision what various potential futures may mean for environments and livelihoods, and in this way facilitate the development of scenarios for the future ( [[#Miller--2015|Miller et al., 2015]] ). Narratives can also play a key role in enabling collective action (Section 1.5) by helping disparate groups co-create a common vision of a desirable future and achieve a common understanding of actions needed to move towards that future ( [[#Linnér--2019|Linnér and Wibeck, 2019]] ; [[#Muiderman--2020|Muiderman et al., 2020]] ).

A narrative contains a storyline in addition to a set of actors ( [[#Elliott--2005|Elliott, 2005]] ). A '''storyline''' is a series of events including their causal connections within a narrative. The IPCC and climate change literature more broadly often use the terms storylines and narratives interchangeably ( [[#O’Neill--2017|]] [[#O’Neill--2017|O’Neill et al., 2017]] ; see also WGI Cross-Chapter Box 6 in Chapter 1; Sections 1.4.4; 10.5.3). A '''scenario storyline''' refers to a narrative description of a scenario including its main characteristics, relationships between driving forces and how these factors evolve (AR6 WGI Section 1.4.4.2, [[#Chen--2021|Chen et al., 2021]] ). Storylines are used to assess risks related to low-likelihood, but high-impact events ( [[#Sutton--2018|Sutton, 2018]] ). In this use of the terms, narratives and storylines do not include specific actors. There is also a critical literature on the use of narratives and storylines based on projected scenarios, which points out the conservative character of these concepts whose performative effect tends to preserve the status quo and the current socioeconomic relationships. ( [[#Malm--2014|Malm and Hornborg, 2014]] ; [[#Chollet--2015|Chollet and Felli, 2015]] ; [[#Lövbrand--2015|Lövbrand et al., 2015]] ; [[#Demortain--2019|Demortain, 2019]] ; [[#Theys--2019|Theys and Cornu, 2019]] ).

Standard research communication may fail to engage policymakers, media and the public at large (WGI AR6 Section 1.2.4, [[#Chen--2021|Chen et al., 2021]] ). Rather, policies and decision making tend to be based on narratives and storylines ( [[#Roe--1994|Roe, 1994]] ; [[#Roe--2017|Roe, 2017]] ). Although mathematical models and narratives are often presumed to be antithetical, in practice they may be complementary and work together ( [[#Morgan--2017|Morgan and Wise, 2017]] ). Communicating research insights through storylines and narratives may have a better chance of transmitting key messages. AR6 employs these communication tools in many places, for instance storylines for constructing and communicating regional climate information or climate services (WGI AR6 Chapter 10, [[#Doblas-Reyes--2021|Doblas-Reyes et al., 2021]] ; WGI AR6 Chapter 12, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ) or ‘low likelihood high warming storylines’ (Chapter 4). To better communicate deep uncertainty in sea level rise projections, WGI uses storylines to describe the physical events that would have to unfold to generate its high-end estimates (Cross-Chapter Box DEEP in Chapter 17).

'''Scenarios''' are defined in IPCC reports as plausible descriptions of how the future may develop, based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technological change, prices) and relationships (Annex II: Glossary). Scenarios are neither predictions nor forecasts but rather ‘foresights’, which imply envisioning challenging futures ( [[#Vervoort--2018|Vervoort and Gupta, 2018]] ). Scenarios are used to provide a view of the potential consequences and implications of developments and actions in a ‘what-if’ mode of exploring the future (AR6 WGIII Section 1.5.1; AR6 WGI Section 1.6.1, [[#Chen--2021|Chen et al., 2021]] ). They may be presented as numerical or mental models. Climate change scenarios are generated by climate modellers to highlight possible alternative GHG emission pathways and are used to develop and integrate projections of emissions and their climate change impacts and for analysing and contrasting climate policy choices. Cross-Chapter Box CLIMATE in [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-1 Chapter 1] describes scenarios used in this report.

Pathways are one element of a larger scenario ( [[#O’Neill--2017|]] [[#O’Neill--2017|O’Neill et al., 2017]] ), focusing on just one element of a larger system of drivers, emissions or concentrations. Scenarios provide one means to represent deep uncertainty when there is disagreement or uncertainty about conceptual models (Cross-Chapter Box DEEP in Chapter 17; [[#IPCC--2019b|IPCC, 2019b]] ). In addition, scenarios provide several important functions in decision support. A lack of strong association with probabilities enables scenarios to promote buy-in from parties to a decision who hold different expectations about the future, helping them to expand the range of futures and options they consider. The process of generating scenarios can serve as the focus of participatory stakeholder exercises and processes, and scenarios can also be used to support risk management by stress-testing alternative policies and identifying robust and adaptive policies under conditions of deep uncertainty (Cross-Chapter Box DEEP in Chapter 17).

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