Editing IPCC:AR6/SR15/Chapter-1 (section)

=== 1.5.2 Assessment Frameworks and Methodologies ===

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''Climate models and associated simulations''

The multiple sources of climate model information used in this assessment are provided in Chapter 2 (Section 2.2) and Chapter 3 (Section 3.2). Results from global simulations, which have also been assessed in previous IPCC reports and that are conducted as part of the World Climate Research Programme (WCRP) Coupled Models Intercomparison Project (CMIP) are used. The IPCC AR4 and Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) reports were mostly based on simulations from the CMIP3 experiment, while the AR5 was mostly based on simulations from the CMIP5 experiment. The simulations of the CMIP3 and CMIP5 experiments were found to be very similar (e.g., Knutti and Sedláček, 2012; Mueller and Seneviratne, 2014) <sup>[[#fn:r284|284]]</sup> . In addition to the CMIP3 and CMIP5 experiments, results from coordinated regional climate model experiments (e.g., the Coordinated Regional Climate Downscaling Experiment, CORDEX) have been assessed and are available for different regions (Giorgi and Gutowski, 2015) <sup>[[#fn:r285|285]]</sup> . For instance, assessments based on publications from an extension of the IMPACT2C project (Vautard et al., 2014; Jacob and Solman, 2017) <sup>[[#fn:r286|286]]</sup> are newly available for 1.5°C projections. Recently, simulations from the ‘Half a degree Additional warming, Prognosis and Projected Impacts’ (HAPPI) multimodel experiment have been performed to specifically assess climate changes at 1.5°C vs 2°C global warming (Mitchell et al., 2016) <sup>[[#fn:r287|287]]</sup> . The HAPPI protocol consists of coupled land–atmosphere initial condition ensemble simulations with prescribed sea surface temperatures (SSTs); sea ice, GHG and aerosol concentrations; and solar and volcanic activity that coincide with three forced climate states: present-day (2006–2015) (see Section 1.2.1) and future (2091–2100) either with 1.5°C or 2°C global warming (prescribed by modified SSTs).

''Detection and attribution of change in climate and impacted systems''

Formalized scientific methods are available to detect and attribute impacts of greenhouse gas forcing on observed changes in climate (e.g., Hegerl et al., 2007; Seneviratne et al., 2012; Bindoff et al., 2013) <sup>[[#fn:r288|288]]</sup> and impacts of climate change on natural and human systems (e.g., Stone et al., 2013; Hansen and Cramer, 2015; Hansen et al., 2016) <sup>[[#fn:r289|289]]</sup> . The reader is referred to these sources, as well as to the AR5 for more background on these methods.

Global climate warming has already reached approximately 1°C (see Section 1.2.1) relative to pre-industrial conditions, and thus ‘climate at 1.5°C global warming’ corresponds to approximately the addition of only half a degree of warming compared to the present day, comparable to the warming that has occurred since the 1970s (Bindoff et al., 2013) <sup>[[#fn:r290|290]]</sup> . Methods used in the attribution of observed changes associate with this recent warming are therefore also applicable to assessments of future changes in climate at 1.5°C warming, especially in cases where no climate model simulations or analyses are available.

Impacts of 1.5°C global warming can be assessed in part from regional and global climate changes that have already been detected and attributed to human influence (e.g., Schleussner et al., 2017) <sup>[[#fn:r291|291]]</sup> and are components of the climate system that are most responsive to current and projected future forcing. For this reason, when specific projections are missing for 1.5°C global warming, some of the assessments of climate change provided in Chapter 3 (Section 3.3) build upon joint assessments of (i) changes that were observed and attributed to human influence up to the present, that is, for 1°C global warming and (ii) projections for higher levels of warming (e.g., 2°C, 3°C or 4°C) to assess the changes at 1.5°C. Such assessments are for transient changes only (see Chapter 3, Section 3.3).

Besides quantitative detection and attribution methods, assessments can also be based on indigenous and local knowledge (see Chapter 4, Box 4.3). While climate observations may not be available to assess impacts from a scientific perspective, local community knowledge can also indicate actual impacts (Brinkman et al., 2016; Kabir et al., 2016) <sup>[[#fn:r292|292]]</sup> . The challenge is that a community’s perception of loss due to the impacts of climate change is an area that requires further research (Tschakert et al., 2017) <sup>[[#fn:r293|293]]</sup> .

''Costs and benefits analysis''

Cost–benefit analyses are common tools used for decision-making, whereby the costs of impacts are compared to the benefits from different response actions (IPCC, 2014a, b) <sup>[[#fn:r294|294]]</sup> . However, for the case of climate change, recognising the complex inter-linkages of the Anthropocene, cost–benefit analysis tools can be difficult to use because of disparate impacts versus costs and complex interconnectivity within the global social-ecological system (see Box 1.1 and Cross-Chapter Box 5 in Chapter 2). Some costs are relatively easily quantifiable in monetary terms but not all. Climate change impacts human lives and livelihoods, culture and values, and whole ecosystems. It has unpredictable feedback loops and impacts on other regions (IPCC, 2014a) <sup>[[#fn:r295|295]]</sup> , giving rise to indirect, secondary, tertiary and opportunity costs that are typically extremely difficult to quantify. Monetary quantification is further complicated by the fact that costs and benefits can occur in different regions at very different times, possibly spanning centuries, while it is extremely difficult if not impossible to meaningfully estimate discount rates for future costs and benefits. Thus standard cost–benefit analyses become difficult to justify (IPCC, 2014a; Dietz et al., 2016) <sup>[[#fn:r296|296]]</sup> and are not used as an assessment tool in this report.

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