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

=== 1.2.3 Linking Science and Society: Communication, Values, and the IPCC Assessment Process ===

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This section assesses how the process of communicating climate information has evolved since AR5. It summarizes key issues regarding scientific uncertainty addressed in previous IPCC assessments and introduces the IPCC calibrated uncertainty language. Next it discusses the role of values in problem-driven, multidisciplinary science assessments such as this one. The section introduces climate services and how climate information can be tailored for greatest utility in specific contexts, such as the global stocktake. Finally, we briefly evaluate changes in media coverage of climate information since AR5, including the increasing role of Internet sources and social media.

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==== 1.2.3.1 Climate Change Understanding, Communication and Uncertainties ====

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Responses to climate change are facilitated when leaders, policymakers, resource managers and their constituencies share a basic understanding of the causes, effects, and possible future course of climate change (SR1.5, [[#IPCC--2018|IPCC, 2018]] ; SRCCL, [[#IPCC--2019a|IPCC, 2019a]] ). Achieving shared understanding is complicated, since scientific knowledge interacts with pre-existing conceptions of weather and climate that have built up in diverse world cultures over centuries, and which are often embedded in strongly held values and beliefs stemming from ethnic or national identities, traditions, religions, and lived relationships to weather, land and sea (Van Asselt and Rotmans, 1996; [[#Rayner--1998|Rayner and Malone, 1998]] ; [[#Hulme--2009|Hulme, 2009]] , 2018; [[#Green--2010|Green et al., 2010]] ; [[#Jasanoff--2010|Jasanoff, 2010]] ; [[#Orlove--2010|Orlove et al., 2010]] ; [[#Nakashima--2012|Nakashima et al., 2012]] ; [[#Shepherd--2020|Shepherd and Sobel, 2020]] ).These diverse, more local understandings can both contrast with and enrich the planetary-scale analyses of global climate science ( ''hi'' ''gh confidence'' ).

Political cultures also give rise to variation in how climate science knowledge is interpreted, used and challenged ( [[#Leiserowitz--2006|Leiserowitz, 2006]] ; [[#Oreskes--2010|Oreskes and Conway, 2010]] ; [[#Brulle--2012|Brulle et al., 2012]] ; [[#Dunlap--2013|Dunlap and Jacques, 2013]] ; [[#Mahony--2014|Mahony, 2014]] , 2015; [[#Brulle--2019|Brulle, 2019]] ). A meta-analysis of 87 studies carried out between 1998 and 2016 (62 USA national, 16 non-USA national, 9 cross-national) found that political orientation and political party identification were the second most important predictors of views on climate change after environmental values (McCright et al. 2016). [[#Ruiz--2020|Ruiz et al. (2020)]] systematically reviewed 34 studies of non-US nations or clusters of nations and 30 studies of the USA alone. They found that in the non-US studies, ‘changed weather’ and ‘socio-altruistic values’ were the most important drivers of public attitudes. For the USA case, by contrast, political affiliation and the influence of corporations were most important. Widely varying media treatment of climate issues also affects public responses ( [[#1.2.3.4|Section 1.2.3.4]] ). In summary, environmental and socio-altruistic values are the most significant influences on public opinion about climate change globally, while political views, political party affiliation, and corporate influence also had strong effects, especially in the USA ( ''hi'' ''gh confidence'' ).

Furthermore, climate change itself is not uniform. Some regions face steady, readily observable change, while others experience high variability that masks underlying trends ( [[#1.4.1|Section 1.4.1]] ); mostregions are subject to hazards, but some may also experience benefits, at least temporarily (Chapters 11, 12 and Atlas). This non-uniformity may lead to wide variation in public climate change awareness and risk perceptions at multiple scales ( [[#Howe--2015|Howe et al., 2015]] ; [[#Lee--2015|Lee et al., 2015]] ). For example, short-term temperature trends, such as cold spells or warm days, have been shown to influence public concern ( [[#Hamilton--2013|Hamilton and Stampone, 2013]] ; [[#Zaval--2014|Zaval et al., 2014]] ; [[#Bohr--2017|Bohr, 2017]] ).

Given these manifold influences and the highly varied contexts of climate change communication, special care is required when expressing findings and uncertainties, including IPCC assessments that inform decision making. Throughout the IPCC’s history, all three Working Groups have sought to explicitly assess and communicate scientific uncertainty ( [[#Le%20Treut--2007|Le Treut et al., 2007]] ; [[#Cubasch--2013|Cubasch et al., 2013]] ). Over time, the IPCC has developed and revised a framework to treat uncertainties consistently across assessment cycles, reports, and Working Groups through the use of calibrated language ( [[#Moss--2000|Moss and Schneider, 2000]] ; [[#IPCC--2005|IPCC, 2005]] ). Since its First Assessment Report (FAR; [[#IPCC--1990a|IPCC, 1990a]] ), the IPCC has specified terms and methods for communicating authors’ expert judgments ( [[#Mastrandrea--2011|Mastrandrea and Mach, 2011]] ). During the AR5 cycle, this calibrated uncertainty language was updated and unified across all Working Groups ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] , 2011). Box 1.1 summarizes this framework as it is used in AR6.

'''Box 1.1 | Treatment of Uncertainty and Calibrated Uncertainty''' '''Language in AR6'''

The AR6 follows the approach developed for AR5 (Box 1.1, Figure 1), as described in the ‘Guidance Notes for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties’ ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ). The uncertainty Guidance Note used in AR6 clarifies the relationship between the qualitative description of confidence and the quantitative representation of uncertainty expressed by the likelihood scale. The calibrated uncertainty language emphasizes traceability of the assessment throughout the process. Key chapter findings presented in each chapter’s Executive Summary are supported in the chapter text by a summary of the underlying literature that is assessed in terms of evidence and agreement, confidence, and also likelihood, if applicable.

In all three Working Groups, author teams evaluate underlying scientific understanding and use two metrics to communicate the degree of certainty in key findings. These metrics are:

# ''confidence:'' a qualitative measure of the validity of a finding, based on the type, amount, quality and consistency of evidence (e.g., data, mechanistic understanding, theory, models, expert judgment) and the degree of agreement.
# ''Likelihood:'' a quantitative measure of uncertainty in a finding, expressed probabilistically (e.g., based on statistical analysis of observations or model results, or both, and expert judgement by the author team or from a formal quantitative survey of expert views, or both).

Throughout IPCC reports, the calibrated language indicating a formal confidence assessment is clearly identified by ''italics'' (e.g., ''medium confidence'' ). Where appropriate, findings can also be formulated as statements of fact without uncertainty qualifiers.

Box.1.1, Figure 1 (adapted from [[#Mach--2017|Mach et al., 2017]] ) shows the idealized step-by-step process by which IPCC authors assess scientific understanding and uncertainties. It starts with the evaluation of the available evidence and agreement (steps 1–2). The following summary terms are used to describe the available evidence: ''limited, medium,'' or ''robust'' ; and the degree of agreement: ''low, medium,'' or ''high'' . Generally, evidence is most robust when there are multiple, consistent, independent lines of high-quality evidence.

If the author team concludes that there is sufficient evidence and agreement, the level of confidence can be evaluated. In this step, assessments of evidence and agreement are combined into a single metric (steps 3–5). The assessed level of confidence is expressed using five qualifiers: ''very low, low, medium, high,'' and ''very high'' . Step 4 depicts how summary statements for evidence and agreement relate to confidence levels. For a given evidence and agreement statement, different confidence levels can be assigned depending on the context, but increasing levels of evidence and degrees of agreement correlate with increasing confidence. When confidence in a finding is assessed to be ''low'' , this does not necessarily mean that confidence in its opposite is ''high,'' and vice versa. Similarly, ''low'' ''confidence'' does not imply distrust in the finding; instead, it means that the statement is the best conclusion based on currently available knowledge. Further research and methodological progress may change the level of confidence in any finding in future assessments.

Ifthe expert judgement of the author team concludes that there is sufficient confidence and quantitative/probabilistic evidence, assessment conclusions can be expressed with likelihood statements (steps 5–6). Unless otherwise indicated, likelihood statements are related to findings for which the authors’ assessment of confidence is ''high'' or ''very high'' . Terms used to indicate the assessed likelihood of an outcome include: ''virtually certain'' : 99–100% probability, ''very likely'' : 90–100%, ''likely'' : 66–100%, ''about as likely as not'' : 33–66%, ''unlikely'' : 0–33%, ''very unlikely'' : 0–10%, ''exceptionally unlikely'' : 0–1%. Additional terms ( ''extremely likely'' : 95–100%, ''more likely than not'' &gt;50–100%, and ''extremely unlikely'' 0–5%) may also be used when appropriate.

Likelihood can indicate probabilities for single events or broader outcomes. The probabilistic information may build from statistical or modelling analyses, other quantitative analyses, or expert elicitation. The framework encourages authors, where appropriate, to present probability more precisely than can be done with the likelihood scale, for example with complete probability distributions or percentile ranges, including quantification of tails of distributions, which are important for risk management (Sections [[#1.2.2|1.2.2]] and [[#1.4.4|1.4.4]] ; [[#Mach--2017|Mach et al., 2017]] ). In some instances, multiple combinations of confidence and likelihood are possible to characterize key findings

Box 1.1

[[File:6793eab315c3a891be05e64e729d221c IPCC_AR6_WGI_Box_1_1_Figure_1.png]]

'''Box 1.1, Figure 1 |'''

'''The IPCC AR6 approach for characterizing understanding and uncertainty in assessment findings.''' This diagram illustrates the step-by-step process authors use to evaluate and communicate the state of knowledge in their assessment ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ). Authors present evidence/agreement, confidence, or likelihood terms with assessment conclusions, communicating their expert judgments accordingly. Example conclusions drawn from Report are presented in the box at the bottom of the figure. Figure adapted from [[#Mach--2017|Mach et al. (2017)]] .
For example, a ''very likely'' statement might be made with ''high confidence'' , whereas a ''likely'' statement might be made with ''very high confidence'' . In these instances, the author teams consider which statement will convey the most balanced information to the reader.

Throughout this WGI Report, unless stated otherwise, uncertainty is quantified using 90% uncertainty intervals. The 90% uncertainty interval, reported in square brackets [x to y], is estimated to have a 90% likelihood of covering the value that is being estimated. The range encompasses the median value and there is an estimated 10% combined likelihood of the value being below the lower end of the range (x) and above its upper end (y). Often the distribution will be considered symmetric about the corresponding best estimate (as in the illustrative example in the figure), but this is not always the case. In this report, an assessed 90% uncertainty interval is referred to as a ‘ ''very likely'' range’. Similarly, an assessed 66% uncertainty interval is referred to as a ‘ ''likely'' range’.

Considerable critical attention has focused on whether applying the IPCC framework effectively achieves consistent treatment of uncertainties and clear communication of findings to users ( [[#Shapiro--2010|Shapiro et al., 2010]] ; [[#Adler--2014|Adler and Hirsch Hadorn, 2014]] ). Specific concerns include, for example, the transparency and traceability of expert judgements underlying the assessment conclusions ( [[#Oppenheimer--2016|Oppenheimer et al., 2016]] ) and the context-dependent representations and interpretations of probability terms ( [[#Budescu--2009|Budescu et al., 2009]] , 2012; [[#Janzwood--2020|Janzwood, 2020]] ). [[#Budescu--2014|Budescu et al. (2014)]] surveyed 25 samples in 24 countries (a total of 10,792 individual responses), finding that even when shown IPCC uncertainty guidance, lay readers systematically misunderstood IPCC likelihood statements. When presented with a ‘high likelihood’ statement, they understood it as indicating a lower likelihood than intended by the IPCC authors. Conversely, they interpreted ‘low likelihood’ statements as indicating a higher likelihood than intended. In another study, British lay readers interpreted uncertainty language somewhat differently from IPCC guidance, but Chinese lay people reading the same uncertainty language translated into Chinese differed much more in their interpretations ( [[#Harris--2013|Harris et al., 2013]] ). Further, even though it is objectively more probable that wide uncertainty intervals will encompass true values, wide intervals were interpreted by lay people as implying subjective uncertainty or lack of knowledge on the part of scientists ( [[#Løhre--2019|Løhre et al., 2019]] ). [[#Mach--2017|Mach et al. (2017)]] investigated the advances and challenges in approaches to expert judgment in AR5. Their analysis showed that the shared framework increased the overall comparability of assessment conclusions across all Working Groups and topics related to climate change, from the physical science basis to resulting impacts, risks, and options for response. Nevertheless, many challenges in developing and communicating assessment conclusions persist, especially for findings drawn from multiple disciplines and Working Groups, for subjective aspects of judgements, and for findings with substantial uncertainties ( [[#Adler--2014|Adler and Hirsch Hadorn, 2014]] ). In summary, the calibrated language cannot entirely prevent misunderstandings, including a tendency to systematically underestimate the probability of the IPCC’s higher-likelihood conclusions and overestimate the probability of the lower-likelihood ones ( ''high confidence'' ). However, a consistent and systematic approach across Working Groups to communicate the assessment outcomes is an important characteristic of the IPCC.

Some suggested alternatives are impractical, such as always including numerical values along with calibrated language ( [[#Budescu--2014|Budescu et al., 2014]] ). Others, such as using positive instead of negative expressions of low-to-medium probabilities, show promise but were not proposed in time for adoption in AR6 ( [[#Juanchich--2020|Juanchich et al., 2020]] ). This report therefore retains the same calibrated language used in AR5 (Box 1.1). Like previous reports, AR6 also includes FAQs that express its chief conclusions in plain language designed for lay readers.

The framework for communicating uncertainties does not allow for indicating cases where ‘deep uncertainty’ is identified in the assessment ( [[#Adler--2014|Adler and Hirsch Hadorn, 2014]] ). The definition of deep uncertainty in IPCC assessments has been described in the context of SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ; Box 5 in [[#Abram--2019|Abram et al., 2019]] ): a situation of deep uncertainty exists when experts or stakeholders do not know or cannot agree on: (i) appropriate conceptual models that describe relationships among key driving forces in a system; (ii) the probability distributions used to represent uncertainty about key variables and parameters; and/or (iii) how to weigh and value desirable alternative outcomes (Cross-Chapter Box 1.2 and Annex VII: Glossary; [[#Abram--2019|Abram et al., 2019]] ). Since AR5, ‘storylines’ or ‘narratives’ approaches have been used to address issues related to deep uncertainty, for example low-likelihood events that would have high impact if they occurred, to better inform risk assessment and decision making ( [[#1.4.4|Section 1.4.4]] ). [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.2.3) notes deep uncertainty in long-term projections for sea level rise, and in processes related to marine ice-sheet instability and marine ice cliff instability.

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==== 1.2.3.2 Values, Science and Climate Change Communication ====

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As noted above, values – fundamental attitudes about what is important, good, and right – play critical roles in all human endeavours, including climate science. In AR5, Chapters 3 and 4 of the WGIII Assessment addressed the role of cultural, social and ethical values in climate change mitigation and sustainable development ( [[#Fleurbaey--2014|Fleurbaey et al., 2014]] ; [[#Kolstad--2014|Kolstad et al., 2014]] ). These values include widely accepted concepts of human rights, enshrined in international law, that are relevant to climate impacts and policy objectives ( [[#Hall--2012|Hall and Weiss, 2012]] ; [[#Peel--2018|Peel and Osofsky, 2018]] ; [[#Setzer--2019|Setzer and Vanhala, 2019]] ). Specific values – human life, subsistence, stability, and equitable distribution of the costs and benefits of climate impacts and policies – are explicit in the texts of the UNFCCC and the PA ( [[#Breakey--2016|Breakey et al., 2016]] ; [[#Dooley--2016|Dooley and Parihar, 2016]] ). Here we address the role of values in how scientific knowledge is created, verified and communicated. Chapters 10, 12 and Cross-Chapter Box 12.2 address how the specific values and contexts of users can be addressed in the co-production of climate information.

The epistemic (knowledge-related) values of science include explanatory power, predictive accuracy, falsifiability, replicability, and justification of claims by explicit reasoning ( [[#Popper--1959|Popper, 1959]] ; [[#Kuhn--1977|Kuhn, 1977]] ). These are supported by key institutional values, including openness, ‘organized scepticism’, and objectivity or ‘disinterestedness’ ( [[#Merton--1973|Merton, 1973]] ), operationalized as well-defined methods, documented evidence, publication, peer review, and systems for institutional review of research ethics (COSEPUP, 2009; [[#Elliott--2017|Elliott, 2017]] ). In recent decades, open data, open code and scientific cyber-infrastructure (notably the Earth System Grid Federation, a partnership of climate modelling centers dedicated to supporting climate research by providing secure, web-based, distributed access to climate model data) have facilitated scrutiny from a larger range of participants, and FAIR data stewardship principles – making data Findable, Accessible, Interoperable and Reusable (FAIR) – are being mainstreamed in many fields ( [[#Wilkinson--2016|Wilkinson et al., 2016]] ). Climate science norms and practices embodying these scientific values and principles include the publication of data and model code, multiple groups independently analysing the same problems and data, model intercomparison projects (MIPs), explicit evaluations of uncertainty, and comprehensive assessments by national academies of science and the IPCC.

The formal Principles Governing IPCC Work (1998, amended 2003, 2006, 2012, 2013) specify that assessments should be ‘comprehensive, objective, open and transparent.’ The IPCC assessment process seeks to achieve these goals in several ways: by evaluating evidence and agreement across all relevant peer-reviewed literature, especially that published or accepted since the previous assessment; by maintaining a traceable, transparent process that documents the reasoning, data and tools used in the assessment; and by maximizing the diversity of participants, authors, experts, reviewers, institutions and communities represented, across scientific discipline, geographical location, gender, ethnicity, nationality and other characteristics. The multi-stage review process is critical to ensure an objective, comprehensive and robust assessment, with hundreds of scientists, other experts and governments providing comments to a series of drafts before the report is finalized.

Social values are implicit in many choices made during the construction, assessment and communication of climate science information ( [[#Heymann--2017|Heymann et al., 2017]] ; [[#Skelton--2017|Skelton et al., 2017]] ). Some climate science questions are prioritized for investigation, or given a specific framing or context, because of their relevance to climate policy and governance. One example is the question of how the effects of a 1.5°C global warming would differ from those of a 2°C warming, an assessment specifically requested by Parties to the PA. The SR1.5 (2018) explicitly addressed this issue ‘within the context of sustainable development; considerations of ethics, equity and human rights; and the problem of poverty’ (Chapters 1 and 5; see also [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al., 2019]] ) following the outcome of the approval of the outline of the Special Report by the IPCC during its 44th Session (Bangkok, Thailand, 17–20 October 2016). Likewise, particular metrics are sometimes prioritized in climate model improvement efforts because of their practical relevance for specific economic sectors or stakeholders. Examples include reliable simulation of precipitation in a specific region, or attribution of particular extreme weather events to inform rebuilding and future policy (Chapters 8 and 11; [[#Intemann--2015|Intemann, 2015]] ; [[#Otto--2018|Otto et al., 2018]] ; [[#James--2019|James et al., 2019]] ). Sectors or groups whose interests do not influence research and modelling priorities may thus receive less information in support of their climate-related decisions ( [[#Parker--2018|Parker and]] [[#Winsberg--2018|Winsberg, 2018]] ).

Recent work also recognizes that choices made throughout the research process can affect the relative likelihood of false alarms (overestimating the probability and/or magnitude of hazards) or missed warnings (underestimating the probability and/or magnitude of hazards), known respectively as Type I and Type II errors. Researchers may choose different methods depending on which type of error they view as most important to avoid, a choice that may reflect social values ( [[#Douglas--2009|Douglas, 2009]] ; [[#Knutti--2018|Knutti, 2018]] ; [[#Lloyd--2018|Lloyd and Oreskes, 2018]] ). This reflects a fundamental trade-off between the values of reliability and informativeness. When uncertainty is large, researchers may choose to report a wide range as ''very likely'' , even though it is less informative about potential consequences. By contrast, high-likelihood statements about a narrower range may be more informative, yet also prove less reliable if new evidence later emerges that widens the range. Furthermore, the difference between narrower and wider uncertainty intervals has been shown to be confusing to lay readers, who often interpret wider intervals as less certain ( [[#Løhre--2019|Løhre et al., 2019]] ).

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==== 1.2.3.3 Climate Information, Co-production and Climate Services ====

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In AR6, ‘climate information’ refers to specific information about the past, current or future state of the climate system that is relevant for mitigation, adaptation and risk management. Cross-Chapter Box 1.1 is an example of climate information at the global scale. It provides climate change information with potential relevance for the global stocktake, and indicates where in AR6 this information may be found.

Responding to national and regional policymakers’ needs for tailored information relevant to risk assessment and adaptation, AR6 emphasizes assessment of regional information more than earlier reports. Here the phrase ‘regional climate information’ refers to predefined reference sets of land and ocean regions; various typological domains (such as mountains or monsoons); temporal frames including baseline periods as well as near term (2021–2040), medium term (2041–2060) and long term (2081–2100); and global warming levels (Chapters 10 and 12, Sections [[#1.4.1|1.4.1]] and [[#1.4.5|1.4.5]] , and Atlas). Regional climate change information is constructed from multiple lines of evidence including observations, paleoclimate proxies, reanalyses, attribution of changes and climate model projections from both global and regional climate models (Sections 1.5.3 and 10.2–10.4). The constructed regional information needs to take account of user context and values for risk assessment, adaptation and policy decisions (Sections 1.2.3 and 10.5).

As detailed in Chapter 10, scientific climate information often requires ‘tailoring’ to meet the requirements of specific decision-making contexts. In a study of the UK Climate Projections 2009 (UKCP09) project, researchers concluded that climate scientists struggled to grasp and respond to users’ information needs because they lacked experience interacting with users, institutions and scientific idioms outside the climate science domain ( [[#Porter--2017|Porter and Dessai, 2017]] ). Economic theory predicts the value of ‘polycentric’ approaches to climate change informed by specific global, regional and local knowledge and experience ( [[#Ostrom--1996|Ostrom, 1996]] , 2012). This is confirmed by numerous case studies of extended, iterative dialogue among scientists, policymakers, resource managers and other stakeholders to produce mutually understandable, usable, task-related information and knowledge, policymaking and resource management around the world ( [[#Lemos--2005|Lemos and Morehouse, 2005]] ; [[#Lemos--2012|Lemos et al., 2012]] , 2014, 2018; see [[#Vaughan--2014|Vaughan and Dessai, 2014]] for a critical view). The SR1.5 (2018) assessed that ‘education, information, and community approaches, including those that are informed by indigenous knowledge and local knowledge, can accelerate the wide-scale behaviour changes consistent with adapting to and limiting global warming to 1.5°C. These approaches are more effective when combined with other policies and tailored to the motivations, capabilities and resources of specific actors and contexts ( ''high confidence'' ).’ These extended dialogic co-production and education processes have thus been demonstrated to improve the quality of both scientific information and governance ( ''high confidence'' ) (Section 10.5 and Cross Chapter Box 12.2).

Since AR5, climate services have increased at multiple levels (local, national, regional and global) to aid decision-making of individuals and organizations and to enable preparedness and early climate change action. These services include appropriate engagement from users and providers, are based on scientifically credible information and producer and user expertise, have an effective access mechanism, and respond to the users’ needs (Glossary; [[#Hewitt--2012|Hewitt et al., 2012]] ). A Global Framework for Climate Services (GFCS) was established in 2009 by the World Meteorological Organization (WMO) in support of these efforts ( [[#Hewitt--2012|Hewitt et al., 2012]] ; [[#Lúcio--2016|Lúcio and Grasso, 2016]] ). Climate services are provided across sectors and time scales, from sub-seasonal to multi-decadal, and support co-design and co-production processes that involve climate information providers, resource managers, planners, practitioners and decision makers ( [[#Brasseur--2016|Brasseur and Gallardo, 2016]] ; [[#Trenberth--2016|Trenberth et al., 2016]] ; C.D. [[#Hewitt--2017|]] [[#Hewitt--2017|Hewitt et al., 2017]] ). For example, they may provide high-quality data on temperature, rainfall, wind, soil moisture and ocean conditions, as well as maps, risk and vulnerability analyses, assessments, and future projections and scenarios. These data and information products may be combined with non-meteorological data, such as agricultural production, health trends, population distributions in high-risk areas, road and infrastructure maps for the delivery of goods, and other socio-economic variables, depending on users’ needs ( [[#WMO--2020a|WMO, 2020a]] ). Cross-Chapter Box 12.2 illustrates the diversity of climate services with three examples from very different contexts.

The current landscapeof climate services is assessed in detail in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] (Section 12.6), with a focus on multi-decadal time scales relevant for climate change risk assessment. Other information relevant to improving climate services for decision-making includes the assessment of methods to construct regional information (Chapter 10), as well as projections at the regional level (Atlas) relevant for impact and risk assessment in different sectors (Chapter 12).

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==== 1.2.3.4 Media Coverage of Climate Change ====

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Climate services focus on users with specific needs for climate information, but most people learn about climate science findings from media coverage. Since AR5, research has expanded on how mass media report climate change and how their audiences respond ( [[#Dewulf--2013|Dewulf, 2013]] ; [[#Jaspal--2014|Jaspal and Nerlich, 2014]] ; [[#Jaspal--2014|Jaspal et al., 2014]] ). For example, in five European Union (EU) countries, television coverage of AR5 used ‘disaster’ and ‘opportunity’ as its principal themes, but virtually ignored the ‘risk’ framing introduced by AR5 WGII ( [[#Painter--2015|Painter, 2015]] ) and now extended by the AR6 (Cross-Chapter Box 1.3). Other studies show that people react differently to climate change news when it is framed as a catastrophe ( [[#Hine--2016|Hine et al., 2016]] ), as associated with local identities ( [[#Sapiains--2016|Sapiains et al., 2016]] ), or as a social justice issue ( [[#Howell--2013|Howell, 2013]] ). Similarly, audience segmentation studies show that responses to climate change vary between groups of people with different, although not necessarily opposing, views on this phenomenon (e.g., [[#Maibach--2011|Maibach et al., 2011]] ; [[#Sherley--2014|Sherley et al., 2014]] ; [[#Detenber--2016|Detenber et al., 2016]] ). In Brazil, two studies have shown the influence of mass media on the high level of public climate change concern in that country (Rodasand Di Giulio, 2017; [[#Dayrell--2019|Dayrell, 2019]] ). In the USA, analyses of television network news show that climate change receives minimal attention, is most often framed in a political context, and largely fails to link extreme weather events to climate change using appropriate probability framing ( [[#Hassol--2016|Hassol et al., 2016]] ). However, recent evidence suggests that Climate Matters (an Internet resource to help US television weather forecasters link weather to climate change trends) may have had a positive effect on public understanding of climate change ( [[#Myers--2020|Myers et al., 2020]] ). Also, some media outlets have recently adopted and promoted terms and phrases stronger than the more neutral ‘climate change’ and ‘global warming’, including ‘climate crisis’, ‘global heating’, and ‘climate emergency’ ( [[#Zeldin-O’Neill--2019|Zeldin-O’Neill, 2019]] ). Google searches on those terms, and on ‘climate action’, increased 20-fold in 2019, when large social movements such as School Strikes forClimate gained worldwide attention ( [[#Thackeray--2020|Thackeray et al., 2020]] ). We thus assess that specific characteristics of media coverage play a major role in climate understanding and perception ( ''high confidence'' ), including how IPCC assessments are received by the general public.

Since AR5, social media platforms have dramatically altered the mass-media landscape, bringing about a shift from uni-directional transfer of information and ideas to more fluid, multi-directional flows ( [[#Pearce--2019|Pearce et al., 2019]] ). A survey covering 18 Latin American countries ( [[#StatKnows-CR2--2019|StatKnows-CR2, 2019]] ) found that the main sources of information about climate change mentioned were the Internet (52% of mentions), followed by social media (18%). There are well-known challenges with social media, such as misleading or false presentations of scientific findings, incivility that diminishes the quality of discussion around climate change topics, and ‘filter bubbles’ that restrict interactions to those with broadly similar views ( [[#Anderson--2017|Anderson and Huntington, 2017]] ). However, at certain moments (such as at the release of the AR5 WGI report), Twitter studies have found that more mixed, highly-connected groups existed, within which members were less polarized ( [[#Pearce--2014|Pearce et al., 2014]] ; [[#Williams--2015|Williams et al., 2015]] ). Thus, social media platforms may in some circumstances support dialogic or co-production approaches to climate communication. Because the contents of IPCC reports speak not only to policymakers, but also to the broader public, the character and effects of media coverage are important considerations across Working Groups.

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