Editing IPCC:AR6/WGI/TS (section)

==== 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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