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

=== 10.6.1 Introduction ===

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This section presents three comprehensive examples of steps for distilling regional climate information from the multiple sources of regional climate information presented in this chapter. These examples build on the general framework presented in [[#10.5|Section 10.5]] , examining in particular the strengths and challenges in linking the different sources, while also exposing the assumptions behind and consequences of decisions made in the process. The examples are framed taking into account societal perspectives that provide context for their regional climate statements. Although the nature of an IPCC Working Group I assessment precludes engaging with users of climate information ( [[#10.5|Section 10.5]] ), we do cite relevant national and regional reports that give user perspectives to set a foundation from which one could distil climate information for users. We have chosen the recent Cape Town drought, Indian summer-monsoon trends and the Mediterranean summer warming because they provide a geographically diverse set of locations and relevant processes and because most of the components for constructing regional climate information outlined in Chapter 10 are directly relevant to each case.

The three comprehensive examples follow a similar structure:

# Motivation and regional context.
# The region’s climate.
# Observational issues.
# Relevant anthropogenic and natural drivers.
# Model simulation and attribution over the historical period.
# Future climate information from global simulations.
# Future climate information from regional downscaling.
# Storylines.
# Climate information distilled from multiple lines of evidence.

Following this structure, construction of the regional climate information presented in these examples depends on an assessment of observational uncertainty relative to the magnitude of a climate change signal ( [[#10.2|Section 10.2]] ), the evaluations of model performance to judge the fitness-for-purpose of a given model ( [[#10.3|Section 10.3]] ), and expert judgement. These factors contribute to attribution of historical climate change signals ( [[#10.4|Section 10.4]] ), recognizing that attribution must account for the interplay between externally forced signals and unforced internal variability. This interplay is explored using multiple model ensembles, including, when appropriate and feasible, single-model initial-condition large ensembles (SMILEs). The multiple lines of evidence for the climate information may conflict, thus requiring distillation of the evidence ( [[#10.5|Section 10.5]] ) to arrive at climate-change statements. When moving from global climate information to climate information at the regional scale, following the structure above provides a basis for arriving at relevant and credible climate information. The comprehensive examples of distilling climate information thus show the value of working with multiple lines of evidence to develop robust climate change information for a region.

In addition to the three comprehensive examples, this section contains two additional examples analysing multiple sources of regional climate information. Box 10.3 on urban climate assesses information that provides a foundation for understanding climatic behaviour in urban areas and its projected change. Cross-Chapter Box 10.4 on climate change over the Hindu Kush Himalaya assembles information rooted in several chapters and previous assessment reports to assess understanding of several climate elements (temperature, precipitation, snow and glaciers, and extreme events) for the region and their projected changes.

As these examples will show, the distillation process of regional climate information from multiple lines of evidence can vary substantially from one case to another. Confidence in the distilled regional climate information is enhanced when there is agreement across multiple lines of evidence, but the outcome of distilling regional climate information can be limited by inconsistent or contradictory sources.

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