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

== 10.5 Combining Approaches to Constructing Regional Climate Information ==

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This section assesses approaches and challenges for producing climate information for climate risk assessments as well as for adaptation and policy decisions at regional scales ( [[#10.1.2.1|Section 10.1.2.1]] ). An overview of the different sources used for developing regional climate information is given in [[#10.5.1|Section 10.5.1]] . The role of the user context in the construction of climate information is assessed in [[#10.5.2|Section 10.5.2]] . The distillation to combine multiple lines of evidence is assessed in [[#10.5.3|Section 10.5.3]] . Finally, climate services in the context of regional climate information are assessed in [[#10.5.4|Section 10.5.4]] . The role of storylines in constructing climate information is assessed in Box 10.2. The assessment of how regional climate information is distilled in the report is treated in Cross-Chapter Box 10.3, whereas the assessment of information on regional, physical climate processes that impact society or ecosystems, termed climatic impact-drivers ( [[#10.1|Section 10.1]] ), appears in Chapter 12, as well as more information on climate services in Cross-Chapter Box 12.2.

The rise in demand for relevant regional climate information ( [[#Hewitt--2012|Hewitt et al., 2012]] , 2020; [[#Lourenço--2016|Lourenço et al., 2016]] ) has resulted in diverse approaches to produce it. Historically, the construction of climate information has been embedded in a linear supply chain: extracting the source data, processing into maps or derived data products, preparing the material for communication, and delivering to users ( [[#10.1.4|Section 10.1.4]] ). Typical products are open-access, web-portal delivery services of data ( [[#Hewitson--2017|Hewitson et al., 2017]] ), which may also be implemented as commercialized climate services ( [[#Webber--2017|Webber and Donner, 2017]] ). Such a chain, although it is intended to meet a demand for regional climate information, contains many assumptions that are not obvious to the recipients and that may introduce possible misunderstandings in the handover from one community to the next ( [[#Meinke--2006|Meinke et al., 2006]] ; [[#Lemos--2012|Lemos et al., 2012]] ). In recognition that data is not necessarily relevant information, a new pathway towards a tailored distillation of climate information has emerged. The construction of information assessed in this section draws on multiple sources (Figure 10.16), whereby the context framing for an application is addressed through co-design with users. The constructed information is then translated into the context of the user taking into account the values of all actors involved (Sections 10.5.2 and 10.5.3, and Figure 10.1).

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[[File:f308beddede012daa796a6f9894df452 IPCC_AR6_WGI_Figure_10_16.png]]

'''Figure 10.16''' '''|''' '''Illustration of how using different sources can result in different and potentially conflicting information.''' Change in daily precipitation (2071–2100 RCP8.5 relative to 1981–2010) over Western Africa as simulated by an ensemble of regional climate models (RCMs) driven by global climate models (GCMs). '''(a)''' Change in daily precipitation (mm) for April to September, as mean of 17 CORDEX models ( [[#Dosio--2020|Dosio et al., 2020]] ) '''(b–e)''' Time-latitude diagram of daily precipitation change for four selected RCM-GCM combinations. For each month and latitude, model results are zonally averaged between 10°W–10°E (blue box in a). Different GCM–RCM combinations can produce substantially different and contrasting results, when the same RCM is used to downscale different GCMs (b, d), or the same GCM is downscaled by different RCMs (d, e). GCM1=IPSL-IPSL-CM5A, GCM2=ICHEC-EC-EARTH, RCM1=RCA4, RCM2=REMO2009. Adapted from [[#Dosio--2020|Dosio et al. (2020)]] , CCBY4.0 https://creativecommons.org/licenses/by/4.0/ . Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

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=== 10.5.1 Sources of Regional Climate Information ===

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Regional climate information may be constructed from a diverse range of sources, each depending on different assumptions and affected by different methodological limitations (Sections 10.2, 10.3 and 10.4). The construction of information may lead to products for direct adoption by users, or intermediate products for further analysis by users and climate services agencies in collaboration with climate scientists. Widely used sources include:

* Extrapolation of observed historical trends into the future (e.g., [[#Livezey--2007|Livezey et al., 2007]] ; [[#Laaha--2016|Laaha et al., 2016]] ). Given that internal variability can affect regional trends significantly on decadal to multi-decadal time scales ( [[#10.4|Section 10.4]] ), this approach could be potentially misleading without other supporting evidence ( [[#Westra--2010|Westra et al., 2010]] ), or finding congruence with other changes (e.g., [[#Langodan--2020|Langodan et al., 2020]] ).
* The output from global models ( [[#10.3.1|Section 10.3.1]] ), including high-resolution GCMs and ESMs, for which performance has been assessed and documented ( [[#10.3.3|Section 10.3.3]] ). Model data can be used in its raw form or may be bias adjusted ( [[#10.3.1|Section 10.3.1]] and Cross-Chapter Box 10.2) or weighted ( [[#10.3.4|Section 10.3.4]] and Box 4.1).
* The output from dynamically ( [[#10.3.1.2|Section 10.3.1.2]] ) or statistically ( [[#10.3.1.3|Section 10.3.1.3]] ) downscaled global model simulations for which performance has been assessed and documented as trustworthy ( [[#10.3.3|Section 10.3.3]] ). Model data can be used in its raw form or may be bias adjusted, in the case of regional climate models (RCMs, [[#10.3.1|Section 10.3.1]] ).
* Process understanding about climate and the drivers of regional climate variability and change, grounded in theory about dynamics, thermodynamics and other physics of the climate system as a basis for process-based evaluation. For instance, teleconnections are useful to understand the links between large and regional scales at both near and long-term depending on the application. (Sections 10.1.3, 10.3.3, 10.4.1, 10.4.3 and Annex IV).
* Idealized scenarios of possible future climates as narratives to explore the implications and consequences of such scenarios in the presence of uncertainty ( [[#Jack--2021|Jack et al., 2021]] ). This approach has been used to explore the response to geoengineering ( [[#Cao--2016a|Cao et al., 2016a]] ), as well as alternative scenarios where model projections are highly uncertain ( [[#Brown--2016|Brown et al., 2016]] ; [[#Jack--2021|Jack et al., 2021]] ).
* Information directly from research reported in the peer-reviewed scientific literature (e.g., [[#Sanderson--2017|Sanderson et al., 2017]] ) or related research reports such as communications to the UN Framework Convention on Climate Change (UNFCCC) about national adaptation.
* Engaging with climate scientists and local communities who may provide indigenous information ( [[#Rosenzweig--2013|Rosenzweig and Neofotis, 2013]] ; [[#Makondo--2018|Makondo and Thomas, 2018]] ).
* Relevant information may also be drawn from paleoclimate studies (e.g., [[#McGregor--2018|McGregor, 2018]] ; Armstrong et al., 2020; [[#Kiem--2020|Kiem et al., 2020]] ) to support and contextualize other sources about more recent and projected changes.

Different sources of information may be more appropriate for some purposes than others, as they may provide information better aligned to the spatial and temporal scales of interest, in different formats, and tailored to different types of application. In some cases, a purpose may be best served using several types of information together. For example, when model data is the primary source, it can be advantageous to employ data from multiple models or even from a range of different experiment types ( [[#10.3.2|Section 10.3.2]] ) supported by assessing how the models reflect changes in driving processes. In this manner a purpose may be best served by seeking the congruence of several types of information together, though one needs to recognize how well the attributes of each source align with the specific need for information. Depending on resources, one may even design model experiments specifically for a given use, such as constructing physical climate storylines of individual events ( [[#10.3.2|Section 10.3.2]] and Box 10.2). Such analyses may be complemented by event attribution studies ( [[IPCC:Wg1:Chapter:Chapter-11#11.1.4|Section 11.1.4]] ).

Users of climate information may face the so-called practitioner’s dilemma: a plethora of different and potentially contrasting sources (Figure 10.16) may be available without a comprehensive and user-relevant evaluation, and these datasets may also lack a transparent and easily understandable explanation of underlying assumptions, strengths and limitations ( [[#Barsugli--2013|Barsugli et al., 2013]] ; [[#Hewitson--2017|Hewitson et al., 2017]] ). Often, the choice of information source is therefore not determined by what is most relevant and informative for the question at hand, but rather by practical constraints such as accessibility and ease of use and may be limited to the availability of just one source in extreme cases ( [[#Rössler--2019a|Rössler et al., 2019a]] ).

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=== 10.5.2 Framing Elements for Constructing User-Relevant Information ===

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==== 10.5.2.1 Consideration of Different Contexts ====

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Without considering the specific context, the distillation of climate information relevant to users may poorly serve the goal of informing adaptation and policy ( [[#Cash--2003|Cash et al., 2003]] ; [[#Lemos--2012|Lemos et al., 2012]] ; [[#Baztan--2017|Baztan et al., 2017]] ). [[#10.1.4|Section 10.1.4]] identifies three implicit framing issues of constructing and delivering user-relevant climate information: practical issues arising from the climate information sources, issues with including the context in constructing the information, and difficulties presented by complex networks of practitioners. The social context strongly influences decisions about constructing information and requires a nuanced and holistic approach to recognize the complexity of a coupled social and physical system ( [[#Daron--2014|Daron et al., 2014]] ). For example, urban water managers must recognize the dependency of the city on different water resources and the interplay of both local and national government legislation that can involve a range of different constituencies and decision makers ( [[#Scott--2018|Scott et al., 2018]] ; [[#Savelli--2021|Savelli et al., 2021]] ).

Context plays a role in determining the risks that may affect human systems and ecosystems and consequently the climate information needs. The context may also limit access to such information. Hence, the context imposes inherent constraints on how climate information can be constructed and optimally aligned with its intended application. Although contexts are unlimited in variety, some key contextual elements include:

* Whether the problem formulation needs to be constructed through consultative activities that, for instance, help identify thresholds of vulnerability in complex urban or rural systems ( [[#Baztan--2017|Baztan et al., 2017]] ; [[#Willyard--2018|Willyard et al., 2018]] ) or is more a matter of addressing a generic vulnerability already identified, such as the frequency of flood events or recurrence intervals of multi-year droughts ( [[#Hallegatte--2013|Hallegatte et al., 2013]] ).
* Societal capacity, such as cultural or institutional flexibility and willingness to respond to different scientific information (e.g., [[#Hart--2012|Hart and Nisbet, 2012]] ; [[#Kahan--2012|Kahan, 2012]] , 2013).
* The technical capability and expertise of the different actors, including users, producers, and communicators (e.g., [[#Sarewitz--2004|Sarewitz, 2004]] ; [[#Gorddard--2016|Gorddard et al., 2016]] ).
* Potential contrasts in value systems such as the different views of the Global North compared to those of economies in transition or under development ( [[#Henrich--2010a|Henrich et al., 2010a]] , b; [[#Sapiains--2021|Sapiains et al., 2021]] ).
* The relative importance of climate change in relation to non-climate stressors on the temporal and spatial scales of interest to the user, which at times are not the ones initially assumed by the producers ( [[#Otto--2015|Otto et al., 2015]] ).
* Availability, timing and accessibility of the required climate information, including the availability of sources such as observations, model simulations, literature and experts of the relevant regional climate ( [[#Mulwa--2017|Mulwa et al., 2017]] ). In developing countries, the availability of all or some of these sources may be limited ( [[#Dinku--2014|Dinku et al., 2014]] ).

These and other contextual elements can frame subsequent decisions about the construction of regional climate information for applications. For example, an engineer typically seeks quantitative information, while the policy community may be more responsive to storylines and how information is positioned within a causal network describing regional climate risk ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.4|Section 1.4.4]] and Box 10.2). Multiple contexts can coexist and potentially result in competing approaches (for example, when urban governance contends with regional water-resource management in the same area).

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==== 10.5.2.2 Developing Climate Information Conditioned by Values of Different Actors and Communities ====

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Developing climate information relevant to user needs can be influenced by the explicit and implicit values of all parties: those constructing the information, those communicating the information, those receiving the information, and, critically, those who construct the problem statement being addressed. A discussion of how values in the scientific community shape climate research appears in [[IPCC:Wg1:Chapter:Chapter-1#1.2.3.2|Section 1.2.3.2]] . The influence of values need not be a source of bias or distortion; it is sometimes appropriate and beneficial: critical scrutiny from a diverse range of value-governing perspectives may uncover and challenge biases and omissions in the information that might otherwise go unrecognized ( [[#Longino--2004|Longino, 2004]] ). Dialogue among all parties in a culturally, socially, and economically heterogeneous society is therefore important for recognizing and reconciling value differences to best yield information that is salient, relevant and avoids ambiguity, most notably when informing the complexity of risks and resilience for human systems and ecosystems in developing nations (e.g., [[#Baztan--2017|Baztan et al., 2017]] ).

Thus, a challenge with constructing climate information for users, especially about impactful change, is that producing the information may need to involve people with a variety of backgrounds, who have different sets of experiences, capabilities, and values. The information thus would need to accommodate and be relevant to a range of different ways of viewing the problem ( [[#Sarewitz--2004|Sarewitz, 2004]] ; [[#Rosenzweig--2013|Rosenzweig and Neofotis, 2013]] ; [[#Gorddard--2016|Gorddard et al., 2016]] ). Failure to recognize the variety of people using the climate information can make it ineffective, even if the source data on which it is based is of the highest quality, and may create a danger of maladaptation.

A substantial body of evidence shows that the receptivity of individuals to climate information is strongly conditioned by motivated reasoning ( [[#Hart--2012|Hart and Nisbet, 2012]] ; [[#Kahan--2012|Kahan, 2012]] , 2013), wherein a person’s reception of climate information is influenced by the values of the community with which the person identifies. Adherence to a community’s values forms part of an individual’s social identity ( [[#Hart--2012|Hart and Nisbet, 2012]] ). Individuals thus frame their analysis and understanding of climate information in the context of cultural values espoused by their community ( [[#Hart--2012|Hart and Nisbet, 2012]] ; [[#Kahan--2012|Kahan, 2012]] , 2013; [[#Campbell--2014|Campbell and Kay, 2014]] ; [[#Bessette--2017|Bessette et al., 2017]] ; [[#Tschakert--2017|Tschakert et al., 2017]] ; [[#Vezér--2018|Vezér et al., 2018]] ). Successful framing of climate information products thus seeks to identify common ground with users, taking account of their values and interests.

Given the relevance of both context and values, the effectiveness of climate information can increase if developed in partnership with the target communities (Figure 10.17; [[#Tschakert--2016|Tschakert et al., 2016]] ). Such an approach can inspire trust among all parties and at the same time promote a co-production process ( [[#Cash--2003|Cash et al., 2003]] ). Recipients of information have the greatest trust when the communicator is perceived as understanding their context and sharing their values and identity ( [[#Corner--2014|Corner et al., 2014]] ). As a consequence, developing mental models informed by user values can help with understanding complex climate models and their outcomes ( [[#Bessette--2017|Bessette et al., 2017]] ).

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[[File:7e2a8db5005a7b53ce95baff79bb7660 IPCC_AR6_WGI_Figure_10_17.png]]

'''Figure 1''' '''0.17 |''' '''Effective regional climate information requires shared development of actionable information that engages all parties involved and the values that guide their engagement.''' Participants in the development of climate information come from varying perspectives, based in part on their professions and communities. Each of the three broad categories shown in the Venn diagram (Users, Producers, Scientists) is not a homogenous group, and often has a diversity of perspectives, values and interests among its members. The subheadings in each category are illustrative and not all-inclusive. The arrows connecting those categories represent the distillation process of providing context and sharing climate relevant information. The arrows that point toward the centre represent the distillation of climate information that involves all three categories.

The importance of a co-production process does not preclude the climate-research community from taking steps to develop and convey relevant information on its own. Indeed, communicating expert consensus about contested scientific issues is beneficial ( [[#Goldberg--2019|Goldberg et al., 2019]] ). Climate services ( [[#10.5.4|Section 10.5.4]] ), in particular, can become an effective means for using sources from the climate community and crafting these to be consistent with the needs, interests and values of stakeholder communities. However, simply presenting more information without recognizing user values and the contextual elements listed in [[#10.5.2.1|Section 10.5.2.1]] may be ineffective ( [[#Kahan--2013|Kahan, 2013]] ). An aversion to climate information discordant with one’s pre-existing beliefs can actually become stronger for people who are more scientifically literate: they feel more confident sifting through all sources of information to find support for their positions ( [[#Kahan--2012|Kahan, 2012]] ). A challenge is that if climate information is not framed carefully, recognizing context and user values, it may make the sceptical person less receptive to further information about climate change ( [[#Corner--2012|Corner et al., 2012]] ; [[#Hart--2012|Hart and Nisbet, 2012]] ; [[#Shalev--2015|Shalev, 2015]] ). A further complication is that audiences may view climate change as a problem distant in time and space ( [[#Spence--2012|Spence et al., 2012]] ), too threatening to acknowledge ( [[#Brügger--2015|Brügger et al., 2015]] ; [[#McDonald--2015|McDonald et al., 2015]] ), or too economically challenging to accept ( [[#Bessette--2017|Bessette et al., 2017]] ). Identifying positive outcomes that align with user values, instead of adaptation and mitigation efforts, appears to promote the interest in and the success of climate information ( [[#Bain--2012|Bain et al., 2012]] ).

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==== 10.5.2.3 The Roles of Spatial and Temporal Resolution in Relation to Decision Scale ====

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Climate processes occur on a range of spatial and temporal scales, from global to local, from centuries and longer to days or less ( [[#10.1.2|Section 10.1.2]] and Figure 10.3). Similarly, decisions by stakeholders cover a range of spatial and temporal scales that can vary with the size of their region of interest and scope of activity. However, the link between decision scales and the spatial and temporal resolution of climate and related non-climatic, natural-system information is not straightforward, and failure to recognize mismatches between the two can undermine the effectiveness and relevance of the information ( [[#Cumming--2006|Cumming et al., 2006]] ; [[#Sayles--2018|Sayles, 2018]] ).

Nevertheless, the scale of regional climate information does not have to be the same as the decision scale. Physical-climate storylines (Box 10.2) valid at large scales can be used to develop understanding that is relevant to local decisions. For example, global climate change affecting Antarctic ice-mass loss is relevant to formulating Dutch responses to sea level rise ( [[#Haasnoot--2020|Haasnoot et al., 2020]] ). On the other hand, extreme precipitation processes can occur on scales of tens of kilometres and smaller and thus require high resolution climate information when projecting future changes (e.g., [[#Xie--2015|Xie et al., 2015]] ). An important factor for developing effective climate information using the distillation process is aligning the vulnerabilities of the social and economic systems under consideration ranging from, for example, those important to a farmer to those important to a national agricultural ministry ( [[#Andreassen--2018|Andreassen et al., 2018]] ; [[#O’Higgins--2019|O’Higgins et al., 2019]] ). Thus, more sophisticated matching of spatial and temporal resolution of climate information with decision scales requires engagement across a hierarchy of governance structures at national, regional and local level (e.g., [[#Lagabrielle--2018|Lagabrielle et al., 2018]] ).

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=== 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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<span id="transdisciplinary-approaches-to-stakeholder-interaction"></span>
==== 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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<span id="barriers-to-the-distillation-of-climate-information"></span>
==== 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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<span id="synthesis-assessment-of-climate-information-distillation"></span>
==== 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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<span id="climate-services-and-the-construction-of-regional-climate-information"></span>
=== 10.5.4 Climate Services and the Construction of Regional Climate Information ===

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Climate services have been defined as the provision of climate information to assist decision-making (Sections 1.2.3, and 12.6, and Cross-Chapter Box 12.2). Services are expected to be based on scientifically credible information and expertise, have appropriate engagement from users and providers, have an effective access mechanism and aim at meeting the users’ needs ( [[#Hewitt--2020|Hewitt et al., 2020]] ). To achieve this, climate services synthesize context-relevant climate information addressing questions for a wide range of climate time scales. From this point of view, climate services are instruments for the production, translation and transfer of climate information and knowledge for their use in climate-informed decision-making and climate-smart policy and planning ( [[#Hewitt--2012|Hewitt et al., 2012]] ). The appropriate provision of climate services considers the diagnosis of climate information needs, the service itself and a number of good practices still under development ( [[#Vaughan--2018|Vaughan et al., 2018]] ).

The preceding subsections assess research on the distillation of climate information, which is directly relevant for the development of climate services. Distillation, when implemented appropriately and interpreted with all due caveats, leads to credible climate information with a broader foundation of evidence to be used in climate services practice according to the recommendations of the Global Framework for Climate Services ( [[#Hewitt--2012|Hewitt et al., 2012]] ). As stated in Chapter 12, climate services set new scientific challenges to research. Examples of some of the challenges have been given in Chapters 1 and 12, which are complemented by the barriers to the distillation assessed in [[#10.5.3.3|Section 10.5.3.3]] .

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'''Box 10.2 | Storylines for Constructing and Communicating Regional Climate Information'''

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Communicating the full extent of available information on future climate for a region, including an uncertainty quantification, can act as a barrier to the uptake and use of such information ( [[#Lemos--2012|Lemos et al., 2012]] ; [[#Daron--2018|Daron et al., 2018]] ). To address the need to simplify and increase the relevance of information for specific contexts, recent studies have adopted storyline and narrative approaches ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.4.2|Section 1.4.4.2]] ; [[#Hazeleger--2015|Hazeleger et al., 2015]] ; [[#Shepherd--2018|Shepherd et al., 2018]] ). As such, these approaches are an important tool for the climate information distillation ( [[#10.5.3|Section 10.5.3]] ). Here we assess these in a regional climate information context, namely for exploring uncertainties, embedding climate information into a given user context, and communicating climate change information.

Physical climate storylines are self-consistent and plausible unfolding of a physical trajectory of the climate system, or a weather or climate event, on time scales from hours to multiple decades ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.4.2|Section 1.4.4.2]] ). Storylines that condition climatic features and processes on a set of plausible but distinct large-scale climatic changes enables the exploration of uncertainties in regional climate projections (Box 10.2, Figure 1 and [[#10.3.4.2|Section 10.3.4.2]] ). For instance, [[#Zappa--2017|Zappa and Shepherd (2017)]] condition projected changes in European surface wind speeds on different plausible projections of tropical upper tropospheric warming and the polar vortex strength in the CMIP5 multi-model ensemble. Storylines of specific events are generated to explore the unfolding and impacts of comparable events in counterfactual climates ( [[#Lackmann--2015|Lackmann, 2015]] ; [[#Meredith--2015b|Meredith et al., 2015b]] ; [[#Takayabu--2015|Takayabu et al., 2015]] ; [[#Hegdahl--2020|Hegdahl et al., 2020]] ; [[#Sillmann--2021|Sillmann et al., 2021]] ). Those event storylines can be based on pseudo-global warming studies ( [[#Lackmann--2015|Lackmann, 2015]] ; [[#Meredith--2015b|Meredith et al., 2015b]] ; [[#Takayabu--2015|Takayabu et al., 2015]] ; see [[#10.3.2.2|Section 10.3.2.2]] ), selected and possibly downscaled events from long-term climate projections ( [[#Hegdahl--2020|Hegdahl et al., 2020]] ; [[#Huang--2020a|Huang et al., 2020a]] ), or based on expert judgment of plausible changes to observed events ( [[#Pisaric--2011|Pisaric et al., 2011]] ; [[#Dessai--2018|Dessai et al., 2018]] ). They can be used for attributing events to different causal factors ( [[#Lackmann--2015|Lackmann, 2015]] ; [[#Meredith--2015b|Meredith et al., 2015b]] ; [[#Takayabu--2015|Takayabu et al., 2015]] ; [[#Trenberth--2015|Trenberth et al., 2015]] ; [[#Shepherd--2016a|Shepherd, 2016a]] ; [[IPCC:Wg1:Chapter:Chapter-11#11.2.4|Section 11.2.4]] ) as well as for exploring the unfolding of events in future climates.

Physical climate storylines are complementary to probabilistic or unconditional risk-based approaches, and are particularly suitable to explore low-likelihood changes or events, which are often associated with the highest impacts ( [[#Shepherd--2018|Shepherd et al., 2018]] ; Sillmann et al., 2020; [[IPCC:Wg1:Chapter:Chapter-4#4.8|Section 4.8]] ). They also facilitate providing local context to large-scale trends and changes, by conditioning the projections on locally relevant circumstances ( [[#Hazeleger--2015|Hazeleger et al., 2015]] ). Storylines are also developed based on expert elicitation and include plausible changes beyond those simulated by existing model projections in order to explore deep uncertainties ( [[#Dessai--2018|Dessai et al., 2018]] ).

Storylines can be combined with impact modelling ( [[#Strasser--2019|Strasser et al., 2019]] ; [[#Hegdahl--2020|Hegdahl et al., 2020]] ) and can be embedded in a user’s risk landscape ( [[#Shepherd--2019|Shepherd, 2019]] ; Box 10.2, Figure 1). In particular, this holds for event storylines, where confounding factors such as regional characteristics like land-use changes and non-climatic drivers of the event are an element of the storyline ( [[#Pisaric--2011|Pisaric et al., 2011]] ; [[#Dessai--2018|Dessai et al., 2018]] ; [[#Lloyd--2020|Lloyd and Shepherd, 2020]] ; [[#Sillmann--2021|Sillmann et al., 2021]] ). In a co-production process, multidisciplinary expert knowledge as well as the values and interests of the intended audiences and stakeholders can be explicitly considered ( [[#Kok--2014|Kok et al., 2014]] ; [[#Bhave--2018|Bhave et al., 2018]] ; [[#Dessai--2018|Dessai et al., 2018]] ; [[#Scott--2018|Scott et al., 2018]] ; [[#Hegdahl--2020|Hegdahl et al., 2020]] ).

Storylines can also be used to communicate climate information by narrative elements describing the main climatological features and the relevant consequences in the user context (Fløttum and Gjerstad, 2017; [[#Moezzi--2017|Moezzi et al., 2017]] ; [[#Dessai--2018|Dessai et al., 2018]] ; [[#Scott--2018|Scott et al., 2018]] ; [[#Jack--2020|Jack et al., 2020]] ). Co-produced narratives have been demonstrated to enhance knowledge integration in decision-making contexts (e.g., [[#de%20Bruijn--2016|de Bruijn et al., 2016]] ). Narrative elements have also been employed to convey information from climate models ( [[#Corballis--2019|Corballis, 2019]] ). [[#Jack--2020|Jack et al. (2020)]] introduced the concept of climate risk narratives and developed a set of principles, such as using present tense in their presentation to avoid the effects of future discounting and writing individual narratives without uncertainty language to assume an imagined observer perspective. From this point of view, event storylines are particularly useful for communication purposes as they link to the experience and episodic memory of stakeholders ( [[#Schacter--2007|Schacter et al., 2007]] ; [[#Steynor--2016|Steynor et al., 2016]] ; [[#Shepherd--2018|Shepherd et al., 2018]] ).

[[File:60e03fa3e1146daa6c3b7280f90ad015 IPCC_AR6_WGI_Box_10_2_Figure_1.png]]

'''Box 10.2,'''

'''Figure 1 |'''

'''Schematic of two types of physical climate storylines with a particular climate impact of concern (red).''' The storylines are defined by specified elements (dark blue). Variable elements (light blue) are simulated conditional on the specified elements. The white elements are ‘blocked’ since their state does not need to be known to determine the light blue elements. Other types of storylines could be defined by specifying other elements (e.g., storylines of different climate sensitivities or different representative concentration pathways). '''(a)''' Event storyline, where the particular dynamical conditions during the event as well as the regional warming are specified and control the hazard arising from the event. '''(b)''' Dynamical storyline, where the global warming level and remote drivers are specified and control the long-term changes in atmospheric dynamics and regional warming. In both storylines, the impact is also conditioned on specified exposure and vulnerability. Figure adapted from [[#Shepherd--2019|Shepherd (2019)]] .

<div id="cross-chapter-box-10.3" class="h2-container box-container"></div>

'''Cross-Chapter Box 10.3 | Assessment of Climate Change Information at the Regional Scale'''

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'''Coordinators:''' Erika Coppola (Italy), Alessandro Dosio (Italy), Friederike Otto (United Kingdom/Germany)

'''Contributors:''' Claudine Dereczynski (Brazil), Melissa I. Gomis (France/Switzerland), Richard G. Jones (United Kingdom), Roshanka Ranasinghe (The Netherlands/Sri Lanka, Australia), Alex C. Ruane (The United States of America), Sonia I. Seneviratne (Switzerland), Anna A. Sörensson (Argentina), Bart van den Hurk (The Netherlands), Robert Vautard (France), Sergio M. Vicente-Serrano (Spain)

This Cross-Chapter Box illustrates how assessments of past, present and future regional climate changes (e.g., change in an extreme event index or climatic impact-driver, CID) are derived in the WGI report. Robust assessments can be derived when changes are supported by multiple lines of evidence.

Multiple, sometimes contrasting, lines of evidence are derived from the various data sources, methodologies and approaches that can be used to construct climate information ( [[#10.5|Section 10.5]] and Figure 10.1). Such data sources and methodologies include theoretical understanding of relevant processes, drivers and feedbacks of climate at regional scale, observed data from multiple datasets (e.g., ground station networks, satellite products, reanalysis, etc.), simulations from different model types (including general circulation models (GCMs), regional climate models (RCMs), statistical downscaling methods, etc.) and experiments (e.g., Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6), Coordinated Regional Climate Downscaling Experiment (CORDEX), and single-model initial-condition large ensembles), methodologies to attribute observed changes or events to large- and regional-scale anthropogenic and natural drivers and forcings as well as other relevant local knowledge (e.g., indigenous knowledge).

[[File:0a1e861f5c874601d7a0f2489c97735f IPCC_AR6_WGI_CCBox_10_3_Figure_1.png]]

'''Cross-Chapter Box 10.3, Figure'''

'''1 |'''

'''Schematic illustration of the process to derive the assessment of regional climate change information based on a distillation process of multiple lines of evidence taken from observed trends, attribution of trends or events, climate model projections, and physical understanding.'''
The assessment is derived following the IPCC uncertainty guidance through a distillation process of multiple lines of evidence on observed trends, attribution of trends or events, climate model projections and physical understanding, covered in several chapters of the WGI Report.

In particular, this Cross-Chapter Box explains the methodology used to derive the regional assessments summarized in the Technical Summary (TS) table that are, in turn, used as a basis for the synthesis assessment in the Summary for Policymakers (SPM).

The process consists of three discrete steps, listed below and schematically illustrated in Cross-Chapter Box 10.3, Figure 1:

'''1. Collection and assessment of the fitness-for-purpose of available information'''

Any specific climate change that is regionally relevant is assessed looking at lines of evidence, potentially across multiple indices. For example, several definitions of ‘drought’ exist that refer to a variety of the underlying processes, temporal and spatial scales, as well as sectoral applications and associated impacts (Sections 11.6 and 12.3). Such diverse definitions need to be gathered from the relevant literature, compared, and individually assessed if appropriate.

Once the indices of change are properly defined, the relevant climate information is collated from the available sources.

The information is then evaluated against its fitness-for-purpose, for example, whether it is adequate to provide ''robust evidence'' to derive an assessment. In the case of observed data, issues to be considered include (but are not limited to): spatial and temporal resolution, accuracy, gaps in the recorded data, homogeneity in the station network, uncertainty treatment, etc. (Sections 10.2, 11.2, 11.9, 12.4; Atlas.1.4). In the case of modelled data, an assessment of the fitness-for-purpose typically includes an evaluation of numerical or statistical methods adopted, adequate representation of the physical processes, forcings and feedbacks relevant for the region and the change under consideration, the availability of adequate ensembles to assess the interplay between forced response and internal variability and the uncertainty in future projections (Sections 10.3, 10.4, 11.2, 11.9, 12.4 and Chapter Atlas). Attribution assessments are usually based on models and observations for which the fitness-for-purpose is assessed with similar criteria as those described above (Cross-Working Group Box: Attribution in Chapter 1). The assessment is made either directly or indirectly by scrutinizing the data and methods of the relevant literature against the criteria listed above.

'''2. Assessment of confidence of the multiple lines of evidence'''

Once the relevant information has been collated for a given regional change, an assessment of the confidence is first made for each line of evidence separately. The assessment of confidence is the result of expert judgment drawing around a set of questions such as:

* Do we have a physical explanation of the processes responsible for past and future changes in the region?
* Do observed trends agree amongst different observational products/datasets? Are they statistically significant? Do the observations cover the same temporal period and/or spatial area? Are the observations homogeneous in time?
* Can past trends be attributed to human activities (greenhouse gases, short-lived climate forcers or land-use/management changes)? Are attributed trends and events consistent? What is the interplay between internal variability and forced response?
* Do model projections agree on the magnitude and sign of the projected signal? Are we able to understand the reasons underlying any discrepancies? Can we quantify the uncertainty in the projected signal? Are the projections based on similar SSP-RCP/time horizon or global warming level (GWL; Cross-Chapter Box 11.1)? If not, are they comparable?
* Has the signal already emerged? Are there studies indicating the time of emergence of the signal?

The assessment is then tested for overall coherence across the available lines of evidence, for example:

* Are observed historical changes consistent with future projections?
* Are attributed events similar to the types of changes projected for the future?
* Is there a physical explanation for changes that are projected but have not yet been clearly observed or attributed?
* Are assessments of confidence and likelihood performed in a similar way across regions?

'''3. Distillation of regional information and synthesis of the independent assessments'''

To ensure transparency, a traceback matrix is constructed (refer to 10.SM) that, for each region and index, identifies where in the chapters the relevant information can be found, together with a summary of the relevant information in the Technical Summary.

Cross-Chapter Box 10.3

Based on assessments mainly in Chapters 8, 9 11, 12 and Atlas, the table in Technical Summary (TS.4.3.1) collates, by means of colours and symbols, the assessment of the confidence in past trend, attribution and direction of future change. This distillation process is illustrated below with two examples: (i) a relatively simple case for the assessment of extreme heat over South-Eastern South America, where most of the lines of evidence agree, and (ii) ecological, agricultural and hydrological drought in the Mediterranean, which is more complex due to the different definitions of ‘drought’ and the sometimes conflicting information arising from different lines of evidence and the example shown here is preceded by the decision to focus on these types of drought rather than, for example, meteorological drought.

'''(a) Extreme heat in South-Eastern South America (SES)'''
Observed past trends

Mean temperature and extreme maximum and minimum temperatures have shown an increasing trend ( ''high confidence'' ). An increase in the intensity and in the frequency of heatwave events between 1961 and 2014 is also observed. However, there is ''medium confidence'' that warm extremes have decreased in the last decades over the central region of SES during austral summer ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] and Atlas.7.2.2).

There is evidence of increasing heat stress during summer in much of SES for the period 1973–2012 ( [[IPCC:Wg1:Chapter:Chapter-12#12.4.4.1|Section 12.4.4.1]] ).

Attribution

Based on trend detection and attribution studies of maximum and minimum temperatures and event attribution of heatwaves in the region, there is ''high confidence'' in a human contribution to the observed increase in the intensity and frequency of hot extremes ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

The increasing heat stress over summer in much of SES has been attributed to human influence on the climate system ( [[IPCC:Wg1:Chapter:Chapter-12#12.4.4.1|Section 12.4.4.1]] ).

Projections

There is ''high confidence'' that by the end of century most regions in South America will undergo extreme heat stress conditions much more often than in the recent past, with about 50–100 more days per year under SSP1-2.6 and more than 200 additional days per year under SSP5-8.5 ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-12#12.4.4.1|Section 12.4.4.1]] ).

Based on different lines of evidence (GCMs, RCMs) an increase in the intensity and frequency of hot extremes is ''extremely likely'' for SES at all assessed warming levels (compared with pre-industrial) ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

Synthesized assessment in the Technical Summary from multiple lines of evidence

There is ''high confidence'' that extreme temperatures have increased over SES over the last decades and that human influence ''likely'' contributed to the observed changes in extreme temperatures. An increase in the frequency and intensity of heatwave events has been observed. Most land regions will frequently undergo extreme heat stress conditions by the end of the 21st century, with an increase in the frequency of heatwaves and heat stress conditions (Technical Summary TS.4.3.2).

'''(b) Mediterranean ecological, agricultural and hydrological droughts'''
Observed past trends

Hydrological modelling suggests that the recent decline in soil moisture in the Mediterranean is unprecedented in the last 250 years. Paleoclimate evidence extends this view, additionally indicating that dryness in the Mediterranean is approaching an extreme condition compared to the last millennium ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.6|Section 8.3.1.6]] ).

There is an increase in probability and intensity of agricultural and ecological droughts ( ''medium confidence'' ) and there is an increase in frequency and severity of hydrological droughts ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

Attribution

Global warming has contributed to drying in dry summer climates including the Mediterranean ( ''high confidence'' ). Records of soil moisture indicate that higher temperatures and increased atmospheric demand have played a strong role in driving Mediterranean aridity. Multiple lines of evidence suggest that anthropogenic forcings are causing increased aridity and drought severity in the Mediterranean region ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.6|Section 8.3.1.6]] ).

Cross-Chapter Box 10.3

An increasing trend towards agricultural and ecological droughts has been attributed to human-induced climate change in the Mediterranean ( ''medium confidence'' ). Model-based assessment shows with ''medium confidence'' a human fingerprint on increased hydrological drought, related to rising temperature and atmospheric demand, and frequency and intensity of recent drought events. There is ''medium confidence'' that change in land-use and terrestrial water management contribute to trends in hydrological drought ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

Projections

There is ''high confidence'' that drought severity and intensity will increase in the Mediterranean. Increased evapotranspiration due to growing atmospheric water demand will decrease soil moisture ( ''high confidence'' ). The seasonality of runoff and streamflow (the annual difference between the wettest and driest months of the year) is expected to increase with global warming ( ''high confidence'' ). Annual runoff is very likely to decrease. Under middle or high-emissions scenarios, the likelihood of extreme droughts increases by 200–300% in the Mediterranean. The paleoclimate record provides context for these future expected changes: climate change will shift soil moisture outside the range of observed and reconstructed values spanning the last millennium ( ''high confidence'' ) (Sections 8.4.1.5 and 8.4.1.6).

There is ''medium confidence'' in the increase of agricultural and ecological drought at +1.5°C, ''high confidence'' at +2°C and ''very likely'' at +4°C, with large decreases in soil water availability during drought events and increase in drought magnitude. There is ''medium confidence'' in the increase in hydrological drought at +1.5°C, ''high confidence'' at +2°C and ''very likely'' at +4°C with very strong decrease (40–60%) of total runoff in the spring-summer half-year and a 50–60% increase in frequency of days under low flow ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

There is ''high confidence'' that agricultural, ecological and hydrological droughts will increase in the Mediterranean region by mid- and end-of-century under all RCPs (except RCP2.6/SSP1-2.6), or for GWLs equal to or higher than 2°C ( [[IPCC:Wg1:Chapter:Chapter-12#12.4.5.2|Section 12.4.5.2]] ).

Synthesized assessment in the Technical Summary from multiple lines of evidence

There is ''high confidence'' that hydrological droughts have increased in the Mediterranean since the 1960s related to rising temperature and atmospheric demand, and ''medium confidence'' of a human fingerprint on this increase. There is ''medium confidence'' in the increase of ecological and agricultural droughts and in their attribution to human-induced climate change. There is ''high confidence'' of an increase in ecological, agricultural and hydrological droughts for warming levels exceeding 2°C, and ''medium confidence'' of an increase for lower warming levels (Technical Summary TS4.3.2).

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<span id="comprehensive-examples-of-steps-toward-constructing-regional-climate-information"></span>