Editing IPCC:AR6/WGI/Chapter-10 (section)

=== 10.5.3 Distillation of Climate Information ===

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The preceding sections laid out the diversity of sources of climate information ( [[#10.5.1|Section 10.5.1]] ) and important elements for its use in a decision context ( [[#10.5.2|Section 10.5.2]] ). Here, it is assessed how context-relevant climate information can be distilled from these sources of information. Although the term distillation lacks a clear definition in the literature, it has, in principle, two aspects: the construction of (potentially user-targeted) information that is defensible and evidence-based ( [[#Giorgi--2020|Giorgi, 2020]] ), and the translation of this information into a specific context, targeting a specific purpose and set of values. The former typically involves data from multiple sources, including expert knowledge, and comprehensively considers relevant uncertainties to give physically plausible climate information. The latter translates the information explicitly into the user context, such as by linking it to experience, by formulating a narrative, by highlighting the relevance for the user context, or by putting the climate information into the context of the relevant non-climatic stressors.

Distilling climate information for a specific purpose benefits from a co-production process that includes non-climate-scientists in the research design, analysis and the exploration and interpretation of the results to best place it in context of the intended application ( [[#Collins--2009|Collins and Ison, 2009]] ; [[#Berkhout--2013|Berkhout et al., 2013]] ; [[#Wildschut--2017|Wildschut, 2017]] ; [[#Bhave--2018|Bhave et al., 2018]] ; [[#Dessai--2018|Dessai et al., 2018]] ). Consideration of the specific contexts of information requirements by the provider as well as including the user values in connecting the science with users is increasingly recognized as paramount to construct information relevant for decisions at the regional scale ( [[#10.5.2|Section 10.5.2]] ; [[#Kruk--2017|Kruk et al., 2017]] ; [[#Vizy--2017|Vizy and Cook, 2017]] ; [[#Djenontin--2018|Djenontin and Meadow, 2018]] ; [[#Parker--2019|Parker and Lusk, 2019]] ; [[#Norström--2020|Norström et al., 2020]] ; [[#Turnhout--2020|Turnhout et al., 2020]] ). As a response, regional climate change information is increasingly being developed through participatory and context-specific dialogues that bring together producers and users across disciplines and define climate impacts as one of the many stressors shaping user decisions ( [[#Brown--2012|Brown and Wilby, 2012]] ; [[#Lemos--2012|Lemos et al., 2012]] ). Although there are multiple practical issues involving communication ( [[#Rössler--2019a|Rössler et al., 2019a]] ), such as providing data in a format that users can interpret, being mindful of the contextual issues raised in [[#10.5.2|Section 10.5.2]] allows non-scientists to be involved in decisions about approaches and assumptions for the distillation and thus to take ownership of the resultant information and to make informed decisions based on the distilled information ( [[#Pettenger--2016|Pettenger, 2016]] ; [[#Verrax--2017|Verrax, 2017]] ). Importantly, the application of transdisciplinary engagement processes that emphasize the role of non-scientists in the learning and knowledge production process builds relationships and trust between information users and producers, which is arguably as important for the uptake of climate science into decision-making as the nature of the climate information itself ( [[#10.5.2|Section 10.5.2]] ).

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==== 10.5.3.1 Information Construction ====

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Data, either from observations or models, is in general not inherently information, but may contain relevant information if interpreted appropriately ( [[#Hewitson--2017|Hewitson et al., 2017]] ). The same applies to other sources of climate information. Relevance is controlled by the given user context ( [[#10.5.2.1|Section 10.5.2.1]] ) and relates to the required temporal and spatial scales ( [[#10.5.2.3|Section 10.5.2.3]] ), the characteristics of required variables (often referred to as indicators), and the meteorological and climatic phenomena driving these variables ( [[#10.1.3|Section 10.1.3]] ). For example, if climate information for driving impact models is sought (e.g., [[#McSweeney--2015|McSweeney et al., 2015]] ), the impact modelling analysis in the target region is the specific user context.

Climate risk assessment considers all plausible outcomes ( [[#Weaver--2017|Weaver et al., 2017]] ; [[#Marchau--2019|Marchau et al., 2019]] ; [[#Sutton--2019|Sutton, 2019]] ). Thus, a key element of information construction is the exploration and reconciliation of different sources of information ( [[#Barsugli--2013|Barsugli et al., 2013]] ; [[#Hewitson--2014b|Hewitson et al., 2014b]] ; [[#Maraun--2018b|Maraun and Widmann, 2018b]] ) and involves mainly two issues: first, assessing the fitness of different sources in the given context and thereby potentially omitting (or down-weighting) selected sources (Sections 10.3.3), and, second, integrating different sources into a broader picture within a context (Sections 10.3.4).

A non-comprehensive selection of approaches that may contribute to the construction of information includes:

* Overall assessment and intercomparison of different sources of information, including hierarchies of models and identification of potentially conflicting results (Figure 10.16), where observational availability plays a critical role ( [[#10.2.3|Section 10.2.3]] ).
* Assessing the emergence of forced trends from internal variability ( [[#10.4.3|Section 10.4.3]] ), and testing whether differences in simulations can be explained by internal variability, ideally using initial-condition large ensembles (Sections 10.3.4.3 and 10.4.3).
* Assessing the interdependence of chosen models to identify the amount of independent information ( [[#10.3.4.4|Section 10.3.4.4]] ).
* Process-based evaluation with focus on those processes that are relevant for the specific application (Sections 10.3.3.4–10.3.3.10).
* Weighting or sub-selecting ensembles based on a priori knowledge or the outcome of a process-based evaluation, while sampling as much uncertainty as possible ( [[#10.3.4.4|Section 10.3.4.4]] ).
* Tracing back differences in projections to the representation of fundamental processes, for example, by using physical climate storylines (Sections 10.3.4.2 and Box 10.2) or sensitivity simulations ( [[#10.3.2.3|Section 10.3.2.3]] ).
* Producing physical-climate storylines (Box 10.2) to explore uncertainties not sampled by available model ensembles ( [[#Shepherd--2018|Shepherd et al., 2018]] ), for example in pseudo-global warming experiments ( [[#10.3.2.2|Section 10.3.2.2]] ), or to simulate events that have never happened before but are nevertheless plausible ( [[#Lin--2016|Lin and Emanuel, 2016]] ).
* Attributing observed changes to different external forcings and internal drivers ( [[#10.4.1|Section 10.4.1]] ).
* Comparing observed trends with past simulated trends in order to constrain projections with, for instance, the Allen–Stott–Kettleborough method ( [[#Allen--2000|Allen et al., 2000]] ; [[#Stott--2002|Stott and Kettleborough, 2002]] ; [[#Stott--2013|Stott et al., 2013]] ) to explain drivers of past observed trends ( [[#10.4.2|Section 10.4.2]] ) for understanding future trends.
* Integrating present-day performance via emergent constraints to reduce projection uncertainty ( [[#10.3.2|Section 10.3.2]] ).
* Complementing the observational and model-based sources with expert judgement (e.g., integrating knowledge from theory or experience that is available from experts or the literature; [[#10.5.1|Section 10.5.1]] ).

These approaches often can be used in combination to increase confidence in conclusions drawn ( [[#Hewitson--2017|Hewitson et al., 2017]] ).

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==== 10.5.3.2 Translating Climate Information Into the User Context ====

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Awareness and understanding of the users’ decision-making context is a central and key aspect of developing tailored, context-appropriate information ( [[#Briley--2015|Briley et al., 2015]] ), as clearly evidenced by the climate services’ experiences (e.g., [[#Vincent--2018|Vincent et al., 2018]] ). Understanding the context, however, is not trivial and requires understanding of both the user and provider ( [[#Guido--2020|Guido et al., 2020]] ) if the information is to be robust, reliable and relevant ( [[#Giorgi--2020|Giorgi, 2020]] ). Translating the information into context requires consideration of terminology and expectations ( [[#Briley--2015|Briley et al., 2015]] ), issues of user interpretation ( [[#Daron--2015|Daron et al., 2015]] ), and hence necessitating engagement in co-production with all attendant challenges ( [[#Vincent--2021|Vincent et al., 2021]] ). The actual provision of climate information may be conducted at different levels of sophistication, ranging from generic data provision via web portals ( [[#Hewitson--2017|Hewitson et al., 2017]] ), potentially including impact-relevant climate indicators, region-specific factsheets and stakeholder reports, social media ( [[#Pearce--2019|Pearce et al., 2019]] ), to a close engagement with specific stakeholders in co-exploring the research ( [[#Steynor--2016|Steynor et al., 2016]] ).

Climate information products may often lack explanations of their potential use and misuse ( [[#Street--2016|Street, 2016]] ; [[#Lamb--2017|Lamb, 2017]] ; [[#Chimani--2020|Chimani et al., 2020]] ). This is particularly important if the information is provided as a generic, publicly accessible product without a specific context ( [[#Hewitson--2017|Hewitson et al., 2017]] ). Context-specific collaboration, especially if organized in workshop, enables a close transdisciplinary co-exploration of the results as in the form of climate risk narratives ( [[#Jack--2020|Jack et al., 2020]] , Box 10.2). Such approaches explicitly account for the user context, values and non-climatic stressors ( [[#Steynor--2019|Steynor and Pasquini, 2019]] ).

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==== 10.5.3.3 Transdisciplinary Approaches to Stakeholder Interaction ====

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The transdisciplinary interaction with stakeholders has been categorized into top-down, bottom-up and interactive approaches ( [[#Berkhout--2013|Berkhout et al., 2013]] ). Traditional top-down approaches frame the research from the perspective of global climate change as a driver of regional climate risk. Bottom-up approaches, also referred to as scenario-neutral impact studies ( [[#Prudhomme--2010|Prudhomme et al., 2010]] ; [[#Brown--2012|]] [[#Brown--2012|A. Brown et al., 2012]] ; [[#Brown--2012|]] [[#Brown--2012|C. Brown et al., 2012]] ; [[#Culley--2016|Culley et al., 2016]] ) begin with the user’s articulation of vulnerability in the context of climatic and non-climatic stressors, follow with the definition of key system thresholds of climatic variables, and only incorporate climate data to assess the likelihood of threshold exceedances. Bottom-up approaches are special cases of robust decision-making ( [[#Lempert--2006|Lempert et al., 2006]] ; [[#Lempert--2007|Lempert and Collins, 2007]] ; [[#Walker--2013|Walker et al., 2013]] ; [[#Weaver--2013|Weaver et al., 2013]] ), which are designed to account for uncertainties not represented by climate models as well as non-climatic stressors. Interactive approaches combine aspects of top-down and bottom-up approaches. The choice of approach depends on the context. While bottom-up approaches might be optimal in a local context, where case-specific risks are addressed, top-down approaches provide generic information that may serve a range of different purposes, for example, at the national scale ( [[#Berkhout--2013|Berkhout et al., 2013]] ). All these approaches benefit from the integration of fully distilled climate information ( [[#Berkhout--2013|Berkhout et al., 2013]] ; [[#Maraun--2018b|Maraun and Widmann, 2018b]] ).

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==== 10.5.3.4 Barriers to the Distillation of Climate Information ====

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As implied by ( [[#10.5.2|Section 10.5.2]] , meeting the needs of users can be a substantial challenge for climate scientists if they misunderstand or have limited understanding of user needs and context ( [[#Porter--2017|Porter and Dessai, 2017]] ). Several barriers in user communities can trigger and sustain this challenge. This can include an institutional aversion to incorporating new tools into decision-making ( [[#Callahan--1999|Callahan et al., 1999]] ). Coincident with this factor, there may be limited staff capacity, lack of management support and lack of a mandate to plan for climate change ( [[#Lee--2010|Lee and Whitely Binder, 2010]] ).

Following from those challenges, constructing and communicating regional climate information often occurs under the overarching assumption that uncertainty is a problem and reducing uncertainty is the priority ( [[#Eisenack--2014|Eisenack et al., 2014]] ; J. [[#Otto--2016|]] [[#Otto--2016|Otto et al., 2016]] ). This is both a psychological ( [[#Morton--2011|Morton et al., 2011]] ) as well as a pragmatic barrier in cases where uncertainty appears to limit the ability to make decisions ( [[#Mukheibir--2007|Mukheibir and Ziervogel, 2007]] ). However, where in-depth engagements with decision contexts are undertaken, these initial barriers are often dismantled to reveal a more complex, nuanced and potentially more productive intersection with climate information producers that can efficiently handle uncertainty (e.g., [[#Rice--2009|Rice et al., 2009]] ; [[#Lemos--2012|Lemos et al., 2012]] ; [[#Moss--2016|Moss, 2016]] ). Specifically, disclosure of all uncertainties in the climate information, transparency about the sources of these uncertainties, and tailoring the uncertainty information to specific decision frameworks have the potential for reducing problems of distilling and communicating uncertain climate information (J. [[#Otto--2016|]] [[#Otto--2016|Otto et al., 2016]] ).

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==== 10.5.3.5 Synthesis Assessment of Climate Information Distillation ====

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There is ''high confidence'' that distilling climate information for a specific purpose benefits from a co-production process that involves users of the information, considers the specific user context and the values of relevant actors such as users and scientists, and translates the resultant information into the broader user context. This process allows users to take ownership of the information, builds relationships and trust between information users and producers and helps to overcome barriers in the information construction. This process enhances trust in the information as well its usefulness, relevance, and uptake, especially when the communication involves complex, contextual details ( ''high confidence'' ). The optimal approach for the transdisciplinary collaboration with users depends on the specific context conditioned by the sources available and the actors involved, which together are dependent on the regions considered and the framing by the question being addressed.

Drawing upon multiple lines of evidence in the construction of climate information increases the fitness of this information and creates a stronger foundation ( ''high confidence'' ). The lines of evidence can include multiple observational datasets, ensembles of different model types, process understanding, expert judgement, and indigenous knowledge, among others. Attribution studies, the characterization of possible outcomes associated with internal variability and a comprehensive assessment of observational, model and forcing uncertainties and possible contradictions using different analysis methods are important elements of distillation. To make the most appropriate decisions and responses to changing climate it is necessary to consider all physically plausible outcomes from multiple lines of evidence, especially in the case when they are contrasting such as in the examples of Cross-Chapter Box 10.1 and [[#10.6.2|Section 10.6.2]] .

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