Editing IPCC:AR6/WGI/Chapter-1 (section)

==== 1.4.5.1 Defining Climate Regions ====

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The AR5 assessed regional-scale detection and attribution and assessed key regional climate phenomena and their relevance for future regional climate projections. This report shows that past and future climate changes and extreme weather events can be substantial on local and regional scales (Chapters 8–12 and Atlas), where they may differ considerably from global trends, not only in intensity but even in the direction of change (e.g., [[#Fischer--2013|Fischer et al., 2013]] ).

Although the evolution of global climate trends emerges as the net result of regional phenomena, average or aggregate estimates often do not reflect the intensity, variability and complexity of regional climate changes ( [[#Stammer--2018|Stammer et al., 2018]] ; [[#Shepherd--2019|Shepherd, 2019]] ). A fundamental aspect of the study of regional climate changes is the definition of characteristic climate zones, clusters or regions, across which the emergent climate change signal can be properly analysed and projected (see Atlas). Suitable sizes and shapes of such zones strongly depend not only on the climate variable and process of interest, but also on relevant multi-scale feedbacks.

There are several approaches to the classification of climate regions. When climate observation data was sparse and limited, the aggregation of climate variables was implicitly achieved through the consideration of biomes, giving rise to the traditional vegetation-based classification of [[#Köppen--1936|Köppen (1936)]] . In the last decades, the substantial increases in climate observations, climate modelling, and data processing capabilities have allowed new approaches to climate classification, for example through interpolation of aggregated global data from thousands of stations ( [[#Peel--2007|Peel et al., 2007]] ; [[#Belda--2014|Belda et al., 2014]] ; [[#Beck--2018|]] [[#Beck--2018|Beck et al., 2018]] ) or through data-driven approaches applied to delineate ecoregions that behave in a coherent manner in response to climate variability ( [[#Papagiannopoulou--2018|Papagiannopoulou et al., 2018]] ). Experience shows that each method has strengths and weaknesses through trade-offs between detail and convenience. For instance, a very detailed classification, with numerous complexly shaped regions derived from a large set of variables, may be most useful for the evaluation of climate models ( [[#Rubel--2010|Rubel and Kottek, 2010]] ; [[#Belda--2015|Belda et al., 2015]] ; [[#Beck--2018|]] [[#Beck--2018|Beck et al., 2018]] ) and climate projections ( [[#Feng--2014|Feng et al., 2014]] ; [[#Belda--2016|Belda et al., 2016]] ). In contrast, geometrically simple regions are often best suited for regional climate modelling and downscaling (e.g., the Coordinated Regional Climate Downscaling Experiment (CORDEX) domains; [[#1.5.3|Section 1.5.3]] ; [[#Giorgi--2015|Giorgi and Gutowski, 2015]] ).

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