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

==== 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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