Quantifying uncertainty in aggregated climate change risk assessments

AbstractHigh-level assessments of climate change impacts aggregate multiple perils into a common framework. This requires incorporating multiple dimensions of uncertainty. Here we propose a methodology to transparently assess these uncertainties within the ‘Reasons for Concern’ framework, using extreme heat as a case study. We quantitatively discriminate multiple dimensions of uncertainty, including future vulnerability and exposure to changing climate hazards. High risks from extreme heat materialise after 1.5–2 °C and very high risks between 2–3.5 °C of warming. Risks emerge earlier if global assessments were based on national risk thresholds, underscoring the need for stringent mitigation to limit future extreme heat risks

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Fig. S1 from Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

Climate scenarios for rainfed maize growing season under the 2.0 °C climate realization across 5 HAPPI GCMs. a-e show changes in temperature and f-j show changes in precipitation. Grid cells with less than 10 ha of maize not shown

Fig. S3 from Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

Difference between 2.0 °C and 1.5 °C on rainfed maize with (left) and without (right) CO₂ effects for three global gridded crop models (pDSSAT, GEPIC, LPJmL)

Fig. S2 from Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

Projected rainfed maize yield change (compared to HAPPI 2006-2015 current period). Columns show different global gridded crop models (pDSSAT, GEPIC, LPJmL), rows show five HAPPI GCMs that provided driving climate projections. Grid cells with less than 10 ha of maize not shown