3.5.3.1 Scenario and sensitivity analysis of climate targets

Probabilistic scenario analysis can be used to assess the risk of overshooting some climate target or to produce probabilistic projections that quantify the likelihood of a particular outcome. Targets for such analysis can be expressed in several different ways: absolute global mean temperature rise by 2100, rate of climate change, other thresholds beyond which dangerous anthropogenic interference (DAI) may occur, or additional numbers of people at risk to various stresses. For example, Arnell et al. (2002) show that such stresses (conversion of forests to grasslands, coastal flood risk, water stress) are far less at 550 ppmv than at 750 ppmv.

Recent Integrated Assessment Models (IAM) literature reflects a renewed attention to climate sensitivity as a key driver of climate dynamics (Den Elzen and Meinshausen, 2006; Hare and Meinshausen, 2006; Harvey, 2006; Keller et al., 2006; Mastrandrea and Schneider, 2004; Meehl et al., 2005; Meinshausen et al., 2006, Meinshausen, 2006; O’Neill and Oppeinheimer, 2002, 2004; Schneider and Lane, 2004; Wigley, 2005). The consideration of a full range of possible climate sensitivity increases the probability of exceeding thresholds for specific DAI. It also magnifies the consequence of delaying mitigation efforts. Hare and Meinshausen (2006) estimate that each 10-year delay in mitigation implies an additional 0.2°C–0.3°C warming over a 100–400 year time horizon. For a climate sensitivity of 3°C, Harvey (2006) shows that immediate mitigation is required to constrain temperature rise to roughly 2°C above pre-industrial levels. Only in the unlikely situation where climate sensitivity is 1°C or lower would immediate mitigation not be necessary.^[26] Harvey also points out that, even in the case of a 2°C threshold (above pre-industrial levels), acidification of the ocean would still occur and that this might not be considered safe.

Another focus of sensitivity analysis is on mitigation scenarios that overshoot and eventually return to a given stabilization or temperature target (Kheshgi, 2004; Wigley, 2005; Harvey, 2004; Izrael and Semenov, 2005; Kheshgi et al., 2005; Meinshausen et al., 2006). Schneider and Mastrandrea (2005) find that this risk of exceeding a threshold of 2ºC above pre-industrial levels is increased by 70% for an overshoot scenario stabilizing at 500 ppmv CO₂-eq (as compared to a scenario stabilizing at 500 ppmv CO₂-eq). Such overshoot scenarios are likely to be necessary if there is a decision to achieve stablization of GHG concentrations close to (or at) today’s levels. They are indeed likely to lower the costs of mitigation but, in turn, raise the risk of exceeding such thresholds (Keller et al., 2006; Schneider and Lane, 2004) and may limit the ability to adapt by increasing the rate of climate change, at least temporarily (Hare and Meinshausen, 2006). O’Neill and Oppenheimer (2004) find that the transient temperature up to 2100 is equally, or more, controlled by the pathway to stabilization than by the stabilization target, and that overshooting can lead to a peak temperature increase that is higher than in the long-term (equilibrium) warming.

The last and important contribution of this approach is to test the sensitivity of results to carbon cycle and climate change feedbacks (Cox et al., 2000; Friedlingstein et al., 2001; Matthews, 2005) and other factors that may affect carbon cycle dynamics, such as deforestation (Gitz and Ciais, 2003). For example, carbon cycle feedbacks amplify warming (Meehl et al., 2007) and are omitted from most other studies that thus underestimate the risks of exceeding (or overshooting) temperature targets for a given effort of mitigation in the energy sector only. This could increase warming by up to 1°C in 2100, according to a simple model (Meehl et al., 2007). The amplification, together with further potential amplification due to feedbacks of uncertain magnitude, such as the potential release of methane from permafrost, peat bogs and seafloor clathrates (Meehl et al., 2007) are also not included in the analysis presented in Figure 3.38 and Table 3.10. This analysis reflects only known feedbacks for which the magnitude can be estimated and are included in General Circulation Models (GCMs). Hence, scenario and sensitivity analysis shows that the risks of exceeding a given temperature threshold for a given temperature target may be higher than that shown in Table 3.10 and Figure 3.38.