We consider the following three sources of uncertainty: equilibrium climate sensitivity, discount rate, and MAC curves. The equilibrium climate sensitivity is assumed to be 3°C by default, with sensitivity cases of 2° and 4.5°C. In comparison to the 1.5° to 4.5°C range, the uncertainty in equilibrium climate sensitivity indicated by the IPCC AR5, our analysis did not consider climate sensitivity below 2°C as suggested by a previous study using ACC2 (74) and recent lines of evidences (77, 78). The discount rate is set at 4% by default and assumed at 2 and 6% in sensitivity cases, spanning a typical range considered in cost-effectiveness analyses (i.e., 5 to 6% in line with market interest rates), as well as low discount rates suggested by recent literature (79). The uncertainty in MAC curves is generally large, and a related study reports an uncertainty range of ±50% in the MAC curves (55). We consider two cases changing the priority of CO2 and non-CO2 mitigation alternately: one case assuming a 50% higher CO2 MAC curve and 50% lower CH4 and N2O MAC curves and the opposite case assuming a 50% lower CO2 MAC curve and 50% higher CH4 and N2O MAC curves. Note that we vary the assumption on the N2O MAC curve for consistency, but this has little influence on the overall results. In the sensitivity analysis, we vary the assumptions on these uncertainties, just one by one from the default, and do not vary more than one assumption at a time owing to the computational burden, yielding a total of seven cases including the default case. A larger number of sources of uncertainty were considered in the historical inversion of the physical part of the model (74). However, in the metric cost analysis demanding more computational resource, we focus on the equilibrium climate sensitivity as the most important uncertain parameter in the physical earth system, while acknowledging that other parameters, including those related to climate-carbon cycle feedbacks, can also be important. Note that, with the climate sensitivity of 4.5°C, the 2°C stabilization pathway and the 1.5°C medium overshoot pathway are not feasible with the abatement constraints put in place. As a result, these two pathways are not considered also in the metric cost analysis when the climate sensitivity is set at 4.5°C, even though these pathways are feasible without the abatement constraints. The largest climate sensitivity that makes these target pathways feasible is 3.4°C in both cases.

In the analysis of best available metrics, we further consider the sensitivity of the assumed set of available metrics. The default set of time horizons considered for GWP and GTP are 100, 50, and 20 years. As a comparison, the IPCC AR5 lists the values of GWP20, GWP100, GTP20, GTP50, and GTP100 for a number of climate forcers in Table 8.A.1. AR5 also reports GWPs and GTPs with a time horizon of 100, 50, 20, and 10 years for a limited number of climate forcers in Table 8.SM.17. In contrast, the previous IPCC Assessment Reports up to AR4 present the values of GWPs with a time horizon of 500, 100, and 20 years (GTP values are only in AR5). We consider the following alternative sets of available time horizons for GWP and GTP: two time horizons (100 and 20 years) and six time horizons (500, 200, 100, 50, 20, and 10 years). In the sensitivity analysis, we choose a time horizon from two or six available time horizons, the CH4 GWP (or GTP) of which is closest to GCP at each point in time. The chosen time horizon is also used for N2O GWP (or GTP) as done in the default analysis. In this exercise, we refer to the AR5 metric values without inclusion of climate-carbon feedbacks for non-CO2 gases. Note that same metric values are assumed in the future period in our analysis. The IPCC metric values have changed over the assessment cycles due to several compounding and competing factors, including improvement in scientific understanding and changing background atmospheric conditions (Section of the IPCC AR5). The metric values will probably be revised also in the future, but such changes are impossible to predict.

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