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

== Executive Summary ==

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Although climate change is a global phenomenon, its manifestations and consequences are different in different regions, and therefore climate information on spatial scales ranging from sub-continental to local is used for impact and risk assessments. Chapter 10 assesses the foundations of how regional climate information is distilled from multiple, sometimes contrasting, lines of evidence. Starting from the assessment of global-scale observations in Chapter 2, Chapter 10 assesses the challenges and requirements associated with observations relevant at the regional scale. Chapter 10 also assesses the fitness of modelling tools available for attributing and projecting anthropogenic climate change in a regional context starting from the methodologies assessed in Chapters 3 and 4. Regional climate change is the result of the interplay between regional responses to both natural forcings and human influence (considered in Chapters 2, 5, 6 and 7), responses to large-scale climate phenomena characterizing internal variability (considered in Chapters 1–9), and processes and feedbacks of a regional nature.

(Chapter 10 is the first of four chapters that assess regional-scale information in this Report. The region-by-region assessment of past and future changes in extremes (Chapter 11), climatic impact-drivers (Chapter 12) and mean climate (Atlas) relies on the sources and methodologies used for constructing regional climate change information assessed in Chapter 10. Building on the assessment of observations and modelling tools of Chapter 10, [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses the observation and modelling of extremes. Chapter 10 assesses methodologies to attribute multi-decadal regional trends to the interplay between external forcing and internal variability, while ( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses the attribution of extreme events. The assessment of climate services in [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] builds on the assessment of distillation of regional climate information from multiple lines of evidence in Chapter 10.

'''Distilling regional climate information from multiple lines of evidence and taking the user context into account will increase the fitness, usefulness and relevance for decis''' '''ion-makin''' '''g and enhances the trust users will have in applying it''' ( ''high confidence'' ''').''' This distillation process can draw upon multiple observational datasets, ensembles of different model types, process understanding, expert judgement and indigenous knowledge. Important elements of distillation include 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. Taking the values of the relevant actors into account when co-producing climate information, and translating this information into the broader user context, improves the usefulness and uptake of this information ( ''high confidence'' ). {10.5}

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=== Observations and Models as Sources of Regional Information ===

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'''The use of multiple sources of observations and tailored diagnostics to evaluate climate model performance increases trust in future projections of regional climate''' ( ''high confidence'' ''').''' The availability of multiple observational records, including reanalyses, that are fit for evaluating the phenomena of interest and account for observational uncertainty, are fundamental for both understanding past regional climate change and assessing climate model performance at regional scales ( ''high confidence'' ). Employing tailored, process-oriented and potentially multivariate diagnostics to evaluate whether a climate model realistically simulates relevant aspects of present-day regional climate increases trust in future projections of these aspects ( ''high confidence'' ). {10.2.2, 10.3.3}

'''Currently, scarcity and reduced availability of adequate observations increase the uncertainty of long-term temperature and precipitation estimates''' ( ''virtually certain'' ''').''' Precipitation measurements in mountainous areas, especially of solid precipitation, are strongly affected by gauge location and setup ( ''very high confidence'' ). Over data-scarce regions or over complex orography, gridded temperature and precipitation products are strongly affected by interpolation methods. Lack of access to the raw station data used to create gridded products compromises the trustworthiness of these products since the influence of the gridding process on the product cannot be assessed. The use of statistical homogenization methods reduces uncertainties related to long-term warming estimates at regional scales ( ''virtually certain'' ). {10.2.2, 10.6.2, 10.6.3, 10.6.4, Box 10.3}

'''Regional reanalyses provide surrogates of observed climate variables that are highly relevant in areas with scarce surface observations.''' Regional reanalyses represent the distributions of precipitation, surface air temperature, and surface wind, including the frequency of extremes, better than global reanalyses ( ''high confidence'' ). However, their usefulness is limited by their short length, the typical regional model errors, and the relatively simple data assimilation algorithms. {Section 10.2.1}

'''Global and regional climate models are important sources of climate information at the regional scale.''' Global models by themselves provide a useful line of evidence for the construction of regional climate information through the attribution or projection of forced changes or the quantification of the role of the internal variability ( ''high confidence'' ). Dynamical downscaling using regional climate models adds value in representing many regional weather and climate phenomena, especially over regions of complex orography or with heterogeneous surface characteristics ( ''very high confidence'' ). Increasing climate model resolution improves some aspects of model performance ( ''high confidence'' ). Some local-scale phenomena such as land–sea breezes and mountain wind systems can only be realistically represented by simulations at a resolution of the order of 10 km or finer ( ''high confidence'' ). Simulations at kilometre-scale resolution add value in particular to the representation of convection, sub-daily precipitation extremes ( ''high confidence'' ) and soil-moisture–precipitation feedbacks ( ''medium confidence'' ). Sensitivity experiments aid the understanding of regional processes and can provide additional user-relevant information. {10.3.3, 10.4, 10.5, 10.6}

'''The performance of global and regional climate models and their fitness for future projections depend on their representation of relevant processes, forcings and drivers and on the specific context.''' Improving global model performance for regional scales is fundamental for increasing their usefulness as regional information sources. It is also key for improving the boundary conditions for dynamical downscaling and the input for statistical approaches, in particular when regional climate change is strongly influenced by large-scale circulation changes. Increasing resolution per se does not solve all performance limitations. Including the relevant forcings (e.g., aerosols, land-use change and stratospheric ozone concentrations) and representing the relevant feedbacks (e.g., snow–albedo, soil-moisture–temperature, soil-moisture–precipitation) in global and regional models is a prerequisite for reproducing historical regional trends and ensuring fitness for future projections ( ''high confidence'' ). The sign of projected regional changes of variables such as precipitation and wind speed is in some cases only simulated in a trustworthy manner if relevant regional processes are represented ( ''medium confidence'' ). {10.3.3, 10.4.1, 10.4.2, 10.6.2, Cross-Chapter Box 10.2}

'''Statistical downscaling, bias adjustment and weather generators are useful approaches for improving the representation of regional climate from dynamical climate models.''' Statistical downscaling methods with carefully chosen predictors and an appropriate model structure for a given application realistically represent many statistical aspects of present-day daily temperature and precipitation ( ''high confidence'' ). Bias adjustment has proven beneficial as an interface between climate model projections and impact modelling in many different contexts ( ''high confidence'' ). Weather generators realistically simulate many statistical characteristics of present-day daily temperature and precipitation, such as extreme temperatures and wet- and dry-day transition probabilities ( ''high confidence'' ). {10.3.3}

'''The performance of statistical downscaling, bias adjustment and weather generators in climate change applications depends on the specific model and on the dynamical climate model driving it.''' Knowledge is still limited about suitable predictors for statistical downscaling of regional climate change, particularly for precipitation. Bias adjustment cannot overcome all consequences of unresolved or strongly misrepresented physical processes, such as large-scale circulation biases or local feedbacks, and may instead introduce other biases and implausible climate change signals ( ''medium confidence'' ). Using bias adjustment as a method for statistical downscaling, particularly for coarse-resolution global models, may lead to substantial misrepresentations of regional climate and climate change ( ''medium confidence'' ). Instead, dynamical downscaling may resolve relevant local processes prior to bias adjustment, thereby improving the representation of regional changes. The performance of statistical approaches and their fitness for future projections depends on predictors and change factors taken from the driving dynamical models ( ''high confidence'' ). {10.3.3, Cross-Chapter Box 10.2}

'''Different types of climate model ensembles allow for the assessment of regional climate projection uncertainties, although ensemble spread is not a full measure of the uncertainty''' ( ''very high confidence'' ''').''' Multi-model ensembles enable the assessment of regional climate response uncertainty ( ''very high confidence'' ). Discarding models that fundamentally misrepresent processes relevant for a given purpose improves the fitness of multi-model ensembles for generating regional climate information ( ''high confidence'' ). At the regional scale, multi-model mean and ensemble spread are not sufficient to characterize low-likelihood, high-impact changes or situations where different models simulate substantially different or even opposing changes ( ''high confidence'' ). In such cases, storylines aid the interpretation of projection uncertainties. Since AR5, the availability of multiple single-model initial-condition large ensembles (SMILEs) allows for a more robust separation of model uncertainty and internal variability in regional-scale projections and provides a more comprehensive spectrum of possible changes associated with internal variability ( ''high confidence'' ). {10.3.4}

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=== Interplay Between Human Influence and Internal Variability at Regional Scales ===

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'''Human influence has been a major driver of regional mean temperature change since 1950 in many sub-continental regions of the world''' ( ''virtually certain'' ''').''' Regional-scale detection and attribution studies as well as observed emergence analysis provide ''robust evidence'' supporting the dominant contribution of human influence to regional temperature changes over multi-decadal periods. {10.4.1, 10.4.3}

'''While human influence has contributed to multi-decadal mean precipitation changes in several regions, internal variability can delay emergence of the anthropogenic signal in long-term precipitation changes in many land regions''' ( ''high confidence'' ''').''' Multiple attribution approaches, including optimal fingerprinting, grid-point detection, pattern recognition and dynamical adjustment methods, as well as multi-model, single-forcing large ensembles and multi-centennial paleoclimate records, support the contribution of human influence to several regional multi-decadal mean precipitation changes ( ''high confidence'' ). At regional scale, internal variability is stronger and uncertainties in observations, models and human influence are all larger than at the global scale, precluding a robust assessment of the relative contributions of greenhouse gases, stratospheric ozone, different aerosol species and land-use/land-cover changes. Multiple lines of evidence, combining multi-model ensemble global projections with those coming from SMILEs, show that internal variability is largely contributing to the delayed or absent emergence of the anthropogenic signal in long-term regional mean precipitation changes ( ''high confidence'' ). {10.4.1, 10.4.2, 10.4.3, 10.6.3, 10.6.4}

'''Various mechanisms operating at different time scales can modify the amplitude of the regional-scale response of temperature, and both the amplitude and sign of the response of precipitation, to human influence''' ( ''high confidence'' ''').''' These mechanisms include non-linear temperature, precipitation and soil moisture feedbacks, slow and fast responses of sea surface temperature patterns and atmospheric circulation changes to increasing greenhouse gases. {10.4.3}

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=== Urban Climate ===

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'''Many types of urban parametrizations simulate radiation and energy exchanges in a realistic way''' ( ''very high confidence'' ''').''' For urban climate studies focusing on the interplay between the urban heat island and regional climate change, a simple single-layer parametrization is fit for purpose ( ''medium confidenc'' e). New networks of monitoring stations in urban areas provide key information to enhance the understanding of urban microclimates and improve urban parametrizations. {Box 10.3}

'''The difference in observed warming trends between cities and their surroundings can partly be attributed to urbanization''' ( ''very high confidence'' ''').''' Annual mean daily minimum temperature is more affected by urbanization than annual mean daily maximum temperature ( ''very high confidence'' ). The global annual mean surface air temperature response to urbanization is, however, negligible ( ''very high confidence'' ) ''.'' {Box 10.3}

'''Future urbanization will amplify the projected air temperature change in cities regardless of the characteristics of the background climate, resulting in a warming signal on minimum temperatures that could be as large as the global warming signal''' ( ''very high confidence'' ''').''' A large effect is expected from the combination of future urban development and more frequent occurrence of extreme climatic events, such as heatwaves ( ''very high confidence'' ). {Box 10.3}

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=== Distillation of Regional Climate Information ===

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'''The process of distilling regional climate information from multiple lines of evidence can vary substantially from one case to another.''' Although methodologies for distillation have been established, in practice the process is conditioned by the sources available, the actors involved and the context, which depend heavily on the regions considered, and is framed by the question being addressed. 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. {10.5, 10.6, Cross-Chapter Box 10.1, Cross-Chapter Box 10.3}

'''confidence in the distilled regional climate information is enhanced when there is agreement across multiple lines of evidence.''' For example, the apparent contradiction between the observed decrease in Indian monsoon rainfall over the second half of the 20th century and the projected long-term increase is explained by attribution of the trends to different forcings, with aerosols dominating recently and greenhouse gases in the future ( ''high confidence'' ). For 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. {10.5.3, 10.6, 10.6.3, 10.6.4, Cross-Chapter Box 10.3}

'''The outcome of distilling regional climate information can be limited by inconsistent or contradictory information.''' Initial observational analyses of the Cape Town drying showed a strong, post-1979 association between increasing greenhouse gases, changes in a key mode of variability (the Southern Annular Mode) and drought in the Cape Town region. However, not all global models show this association, and subsequent analysis extending farther back in time, when human influence was weaker, showed no strong association in observations between the Southern Annular Mode and Cape Town drought. Thus, despite the consistency among global-model future projections, there is ''medium confidence'' in a projected future drier climate for Cape Town. Likewise, the distillation process results in ''low confidence'' in the influence of Arctic warming on mid-latitude climate because of contrasting lines of evidence. {10.5.3, 10.6.2, Cross-Chapter Box 10.1, Cross-Chapter Box 10.3}

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