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

=== 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]] ).

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'''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)]] .

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'''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).

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'''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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