Editing IPCC:AR6/WGI/TS (section)

=== TS.4.1 Generation and Communication of Regional Climate Change Information ===

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'''Climate change information at regional scale is generated using a range of data sources and methodologies. Multi-model ensembles and models with a range of resolutions are important data sources, and discarding models that fundamentally misrepresent relevant processes improves the credibility of ensemble information related to these processes. A key methodology is distillation – combining lines of evidence and accounting for stakeholder context and values – which helps ensure the information is relevant, useful and trusted for decision-making (see Core Concepts Box) ( ''high confidence'' ).''' '''Since AR5, physical climate storylines have emerged as a complementary approach to ensemble projections for generating more accessible climate information and promoting a more comprehensive treatment of risk. They have been used as part of the distillation process within climate services to generate the required context-relevant, credible and trusted climate information.''' '''Since AR5, climate change information produced for climate services has increased significantly due to scientific and technological advancements and growing user awareness, requirements, and demand ( ''very high confidence'' ). The decision-making context, level of user engagement, and co-production between scientists, practitioners and users are important determinants of the type of climate service developed and its utility in supporting adaptation, mitigation and risk management decisions. Links to chapters 10.3, 10.6, Cross-Chapter Box 10.3, 12.6, Cross-Chapter Box 12.2'''
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==== TS.4.1.1 Sources and Methodologies for Generating Regional Climate Information ====

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Climate change information at regional scale is generated using a range of data sources and methodologies (Section TS.1.4). Understanding of observed regional climate change and variability is based on the availability and analysis of multiple observational datasets that are suitable for evaluating the phenomena of interest (e.g., extreme events), including accounting for observational uncertainty (Section TS.1.2.1). These datasets are combined with climate model simulations of observed changes and events to attribute causes of those changes and events to large- and regional-scale anthropogenic and natural drivers and to assess the performance of the models. Future simulations with many climate models (multi-model ensembles) are then used to generate and quantify ranges of projected regional climate responses (Section TS.4.2). Discarding models that fundamentally misrepresent relevant processes improves the credibility of regional climate information generated from these ensembles ( ''high confidence'' ). However, multi-model mean and ensemble spread are not a full measure of the range of projection uncertainty and are not sufficient to characterize low-likelihood, high-impact changes (Box TS.3) or situations where different models simulate substantially different or even opposite changes ( ''high confidence'' ) ''.'' Large single-model ensembles are now available and provide a more comprehensive spectrum of possible changes associated with internal variability ( ''high confidence'' ) (Section TS.1.2.3). Links to chapters 1.5.1, 1.5.4, 10.2, 10.3.3, 10.3.4, 10.4.1, 10.6.2, 11.2, Box 11.2, Cross-Chapter Box 11.1, 12.4, Atlas.1.4.1

Depending on the region of interest, representing regionally important forcings (e.g., aerosols, land-use change and ozone concentrations) and feedbacks (e.g., between snow and albedo, soil moisture and temperature, or soil moisture and precipitation) in climate models is a prerequisite for them to reproduce past regional trends to underpin the reliability of future projections ( ''medium confidence'' ) (Section TS.1.2.2). In some cases, even the sign of a projected change in regional climate cannot be trusted if relevant regional processes are not represented, for example, for variables such as precipitation and wind speed ( ''medium confidence'' ) ''.'' In some regions, either geographical (e.g., Central Africa, Antarctica) or typological (e.g., mountainous areas, Small Islands and cities), and for certain phenomena, fewer observational records are available or accessible, which limits the assessment of regional climate change in these cases. Links to chapters 1.5.1, 1.5.3, 1.5.4, 8.5.1, 10.2, 10.3.3, 10.4.1, 11.1.6, 11.2, 12.4, Atlas.8.3, Atlas.11.1.5, Cross-Chapter Box Atlas.2

Methodologies such as statistical downscaling, bias adjustment and weather generators are beneficial as an interface between climate model projections and impact modelling and for deriving user-relevant indicators ( ''high confidence'' ). However, the performance of these techniques depends on that of the driving climate model: in particular, bias adjustment cannot overcome all consequences of unresolved or strongly misrepresented physical processes, such as large-scale circulation biases or local feedbacks ( ''medium confidence'' ). Links to chapters 10.3.3, Cross-Chapter Box 10.2, 12.2, Atlas.2.2

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'''Box TS.10 | Event Attribution'''

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'''The attribution of observed changes in extremes to human influence (including greenhouse gas and aerosol emissions and land-use changes) has substantially advanced since AR5, in particular for extreme precipitation, droughts, tropical cyclones, and compound extremes ( ''high confidence'' ). There is limited evidence for windstorms and convective storms. Some recent hot extreme events would have been ''extremely unlikely'' to occur without human influence on the climate system. (Section TS.1) Links to chapters Cross-Working Group Box: Attribution in Chapter 1, 11.2, 11.3, 11.4, 11.6, 11.7, 11.8'''
Since AR5, the attribution of extreme weather events has emerged as a growing field of climate research with an increasing body of literature. It provides evidence that greenhouse gases and other external forcings have affected individual extreme weather events by disentangling anthropogenic drivers from natural variability. Event attribution is now an important line of evidence for assessing changes in extremes on regional scales. (Section TS.1) Links to chapters Cross-Working Group Box: Attribution, 11.1.4

The regional extremes and events that have been studied are geographically uneven (Section TS.4.1). A few events, for example, extreme rainfall events in the United Kingdom, heatwaves in Australia, or Hurricane Harvey that hit Texas in 2017, have been heavily studied. Many highly impactful extreme weather events have not been studied in the event attribution framework, particularly in the developing world where studies are generally lacking. This is due to various reasons, including lack of observational data, lack of reliable climate models, and lack of scientific capacity. While the events that have been studied are not representative of all extreme events that have occurred, and results from these studies may also be subject to selection bias, the large number of event attribution studies provide evidence that changes in the properties of these local and individual events are in line with expected consequences of human influence on the climate and can be attributed to external drivers. Links to chapters Cross-Working Group Box: Attribution, 11.1.4, 11.2.2

It is ''very likely'' that human influence is the main contributor to the observed increase in the intensity and frequency of hot extremes and the observed decrease in the intensity and frequency of cold extremes on continental scales. Some specific recent hot extreme events would have been ''extremely unlikely'' to occur without human influence on the climate system. Changes in aerosol concentrations have ''likely'' slowed the increase in hot extremes in some regions, in particular from 1950–1980. No-till farming, irrigation and crop expansion have similarly attenuated increases in summer hot extremes in some regions, such as central North America ( ''medium confidence'' ). Links to chapters 11.3.4

Human influence has contributed to the intensification of heavy precipitation in three continents where observational data are most abundant: North America, Europe and Asia ( ''high confidence'' ). On regional scales, evidence of human influence on extreme precipitation is limited, but new evidence from attributing individual heavy precipitation events found that human influence was a significant driver of the events. Links to chapters 11.4.4

There is ''low confidence'' that human influence has affected trends in meteorological droughts in most regions, but ''medium confidence'' that they have contributed to the severity of some specific events. There is ''medium confidence'' that human-induced climate change has contributed to increasing trends in the probability or intensity of recent agricultural and ecological droughts, leading to an increase of the affected land area. Links to chapters 11.6.4

Event attribution studies of specific strong tropical cyclones provide ''limited evidence'' for anthropogenic effects on tropical cyclone intensifications so far, but ''high confidence'' for increases in precipitation. There is ''high confidence'' that anthropogenic climate change contributed to extreme rainfall amounts during Hurricane Harvey (in 2017) and other intense tropical cyclones. Links to chapters 11.7.3

The number of evident attribution studies on compound events is limited. There is ''medium confidence'' that weather conditions that promote wildfires have become more probable in southern Europe, northern Eurasia, the USA, and Australia over the last century. In Australia a number of event attribution studies show that there is ''medium confidence'' of increase in fire weather conditions due to human influence. Links to chapters 11.8.3, 12.4.3.2

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'''Box TS.10, Figure 1 |'''

'''Synthesis of assessed observed and attributable regional changes.''' The IPCC AR6 WGI inhabited regions are displayed as '''hexagons''' of identical sizes in their approximate geographical location (see legend for regional acronyms). All assessments are made for each region as a whole and for the 1950s to the present. Assessments made on different time scales or more local spatial scales might differ from what is shown in the figure. The '''colours''' in each panel represent the four outcomes of the assessment on observed changes. Striped hexagons (white and light-grey) are used where there is ''low agreement'' in the type of change for the region as a whole, and grey hexagons are used when there is limited data and/or literature that prevents an assessment of the region as a whole. Other colours indicate at least ''medium confidence'' in the observed change. The '''confidence level''' for the human influence on these observed changes is based on assessing trend detection and attribution and event attribution literature, and it is indicated by the number of dots: three dots for ''high confidence'' , two dots for ''medium confidence'' and one dot for ''low confidence'' (single, filled dot: limited agreement; single, empty dot: ''limited evidence'' ).
'''Panel (a) For hot extremes,''' the evidence is mostly drawn from changes in metrics based on daily maximum temperatures; regional studies using other indices (heatwave duration, frequency and intensity) are used in addition. Red hexagons indicate regions where there is at least ''medium confidence'' in an observed increase in hot extremes.

'''Panel (b) For heavy precipitation,''' the evidence is mostly drawn from changes in indices based on one-day or five-day precipitation amounts using global and regional studies. Green hexagons indicate regions where there is at least ''medium confidence'' in an observed increase in heavy precipitation.

'''Panel (c) Agricultural and ecological droughts''' are assessed based on observed and simulated changes in total column soil moisture, complemented by evidence on changes in surface soil moisture, water balance (precipitation minus evapotranspiration) and indices driven by precipitation and atmospheric evaporative demand. Yellow hexagons indicate regions where there is at least ''medium confidence'' in an observed increase in this type of drought and green hexagons indicate regions where there is at least ''medium confidence'' in an observed decrease in agricultural and ecological drought.

For all regions, Table TS.5 shows a broader range of observed changes besides the ones shown in this figure. Note that Southern South America (SSA) is the only region that does not display observed changes in the metrics shown in this figure, but is affected by observed increases in mean temperature, decreases in frost and increases in marine heatwaves. (Table TS.5) Links to chapters 11.9, Atlas, 1.3.3, Figure Atlas.2

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==== TS.4.1.2 Regional Climate Information Distillation and Climate Services ====

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The construction of regional climate information involves people with a variety of backgrounds, from various disciplines, who have different sets of experiences, capabilities and values. The process of synthesizing climate information from different lines of evidence from a number of sources, taking into account the context of a user vulnerable to climate variability and change and the values of all relevant actors, is called distillation. Distillation is conditioned by the sources available, the actors involved, and the context, which all depend heavily on the regions considered, and is framed by the question being addressed. Distilling regional climate information from multiple lines of evidence and taking the user context into account increases fitness, usefulness, relevance and trust in that information for use in climate services (Box TS.11) and decision-making ( ''high confidence'' ). Links to chapters 1.2.3, 10.1.4, 10.5, Cross-Chapter Box 10.3, 12.6

The distillation process can vary substantially, as it needs to consider multiple lines of evidence on all physically plausible outcomes (especially when they are contrasting) relevant to a specific decision required in response to a changing climate. Confidence in the distilled regional climate information is enhanced when there is agreement across multiple lines of evidence, so the outcome can be limited if these are inconsistent or contradictory. For example, in the Mediterranean region the agreement between different lines of evidence, such as observations, projections by regional and global models, and understanding of the underlying mechanisms, provides ''high confidence'' in summer warming that exceeds the global average (see Box TS.12). In a less clear-cut case for Cape Town, South Africa, despite consistency among global model future projections, there is ''medium confidence'' in a projected future drier climate due to the lack of consistency in links between increasing greenhouse gases, changes in a key mode of variability (the Southern Annular Mode) and drought in Cape Town among different observation periods and in model simulations. Links to chapters 10.5.3, 10.6, 10.6.2, 10.6.4, Cross-Chapter Box 10.3, 12.4

Since AR5, physical climate storyline approaches have emerged as a complementary instrument to provide a different perspective, or additional climate information, to facilitate communication of the information or provide a more flexible consideration of risk. Storylines that condition climatic events and processes on a set of plausible but distinct large-scale climatic changes enable the exploration of uncertainties in regional climate projections. For example, they can explicitly address low-likelihood, high-impact outcomes, which would be less emphasized in a probabilistic approach, and can be embedded in a user’s risk landscape, taking account of socio-economic factors as well as physical climate changes. Storylines can also be used to communicate climate information by narrative elements describing and contextualizing the main climatological features and the relevant consequences in the user context and, as such, can be used as part of a climate information distillation process. Links to chapters 1.4.4., Box 10.2, 11.2, Box 11.2, Cross-Chapter Box 12.2

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'''Box TS.11 | Climate Services'''

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'''Climate services involve providing climate information to assist decision-making, for example, about how extreme rainfall will change to inform improvements in urban drainage. Since AR5, there has been a significant increase in the range and diversity of climate service activities ( ''very'' ''high confidence'' ). The level of user-engagement, co-design and co-production are factors determining the utility of climate services, while resource limitations for these activities constrain their full potential. Links to chapters 12.6, Cross-Chapter Box 12.2'''
Climate services include engagement from users and providers and an effective access mechanism; they are responsive to user needs and based on integrating scientifically credible information and relevant expertise. Climate services are being developed across regions, sectors, time scales and user-groups and include a range of knowledge brokerage and integration activities. These involve identifying knowledge needs; compiling, translating and disseminating knowledge; coordinating networks and building capacity through informed decision-making; analysis, evaluation and development of policy; and personal consultation.

Since AR5, climate change information produced in climate service contexts has increased significantly due to scientific and technological advancements and growing user awareness, requirements and demand ( ''very'' ''high confidence'' ). Climate services are growing rapidly and are highly diverse in their practices and products. The decision-making context, level of user engagement and co-production between scientists, practitioners and intended users are important determinants of the type of climate service developed and their utility for supporting adaptation, mitigation and risk management decisions. They require different types of user–producer engagement depending on what the service aims to deliver ( ''high confidence'' ), and these fall into three broad categories: website-based services, interactive group activities and focused relationships ''.''

Realization of the full potential of climate services is often hindered by limited resources for the co-design and co-production process, including sustained engagement between scientists, service providers and users ( ''high confidence'' ). Further challenges relate to the development and provision of climate services, generation of climate service products, communication with users, and evaluation of their quality and socio-economic benefit. (Section TS.4.1) Links to chapters 1.2.3, 10.5.4, 12.6, Cross-Chapter Box 12.2, Glossary

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'''Box TS.12 | Multiple Lines of Evidence for Assessing Regional Climate Change and the''' '''Interactive''' '''Atlas'''

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'''A key novel element in the AR6 is the Working Group I Atlas, which includes the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] ( https://interactive-atlas.ipcc.ch/ ). The Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] provides the ability to explore much of the observational and climate model data used as lines of evidence in this assessment to generate regional climate information. Links to chapters Atlas.2'''
A significant innovation in the AR6 WGI Report is the Atlas. Part of its remit is to provide region-by-region assessment on changes in mean climate and to link with other WGI chapters to generate climate change information for the regions. An important component is the new online interactive tool, the Interactive Atlas, with flexible spatial and temporal analyses of much of the observed, simulated past and projected future climate change data underpinning the WGI assessment. This includes the ability to generate global maps and a number of regionally aggregated products (time series, scatter plots, tables, etc.) for a range of observations and ensemble climate change projections of variables (such as changes in the climatic impact-drivers summarized in Table TS.5) from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5, CMIP6) and the Coordinated Regional Climate Downscaling Experiment (CORDEX). The data can be displayed and summarized under a range of SSP-RCP scenarios and future time slices and also for different global warming levels, relative to several different baseline periods. The maps and various statistics can be generated for annual mean trends and changes or for any user-specified season. A new set of WGI reference regions is used for the regional summary statistics and applied widely throughout the report (with the regions, along with aggregated datasets and the code to generate these, available at the ATLAS GitHub: https://github.com/IPCC-WG1/Atlas ).

Box TS.12, Figure 1 shows how the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] products, together with other lines of evidence, can be used to generate climate information for an illustrative example of the Mediterranean summer warming. The lines of evidence include the understanding of relevant mechanisms, dynamic and thermodynamic processes and the effect of aerosols in this case (Box TS.12, Figure 1a); trends in observational datasets (which can have different spatial and temporal coverage; Box TS.12, Figure 1b, c); and attribution of these trends and temperature projections from global and regional climate models at different resolutions, including single-model initial-condition large ensembles (SMILEs; Box TS.12, Figure 1d, e). Taken together, this evidence shows there is ''high confidence'' that the projected Mediterranean summer temperature increase will be larger than the global mean, with consistent results from CMIP5 and CMIP6 (Box TS.12, Figure 1e). However, CMIP6 results project both more pronounced warming than CMIP5 for a given emissions scenario and time period and a greater range of changes (Box TS.12, Figure 1d). Links to chapters 10.6.4, Atlas.2, Atlas.8.4

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'''Box TS.12, Figure 1 |'''

'''Example of generating regional climate information from multiple lines of evidence for the case of Mediterranean summer warming.''' Box TS.12 ''The intent of this figure is to provide an example of using different lines of evidence to assess the confidence in or likelihood of a projected change in regional climate and which of these lines of evidence are available to view and explore in the Interactive Atlas.'' '''(a)''' Mechanisms and feedbacks involved in enhanced Mediterranean summer warming. '''(b)''' Locations of observing stations from different datasets. '''(c)''' Distribution of 1960–2014 summer temperature trends (°C per decade) for observations (black crosses), CMIP5 (blue circles), CMIP6 (red circles), HighResMIP (orange circles), CORDEX EUR-44 (light blue circles), CORDEX EUR-11 (green circles), and selected single model initial-condition large ensembles (SMILEs; grey boxplots, MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF). '''(d)''' Time series of area averaged (25°N–50°N, 10°W–40°E) land point summer temperature anomalies (°C, baseline period is 1995–2014): the boxplot shows long term (2081–2100) temperature changes of different CMIP6 scenarios in respect to the baseline period. '''(e)''' Projected Mediterranean summer warming in comparison to global annual mean warming of CMIP5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) and CMIP6 (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) ensemble means (lines) and spread (shading). Links to chapters Figure 10.20, Figure 10.21, Figure Atlas.8

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