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

=== 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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