3.5.2 Linking emission scenarios to changes in global mean temperature, impacts and key vulnerabilities

In a risk-management framework, a first step to understanding the environmental consequences of mitigation strategies is to look at links between various stabilization levels for concentrations of greenhouse gases in the atmosphere, and the global mean temperature change relative to a particular baseline. A second step is to link levels of temperature change and key vulnerabilities. Climate models indicate significant uncertainty at both levels. Figure 3.38 shows CO₂-eq concentrations that would limit warming at equilibrium below the temperatures indicated above pre-industrial levels, for ‘best estimate’ climate sensitivity, and for the likely range of climate sensitivity (see Meehl et al., 2007, Section 10.7, and Table 10.8; and the notes to Figure 3.38). It also shows the corresponding radiative forcing levels and their relationship to equilibrium temperature and CO₂-eq concentrations. The table and the figure illustrate how lower temperature constraints require lower stabilization levels, and also that, if the potential for climate sensitivities is higher than the ‘best estimate’ and is taken into account, the constraint becomes more stringent. These more stringent constraints lower the risks of exceeding the threshold.

Figure 3.38 and Table 3.10 provide an overview of how emission scenarios (Section 3.3) relate to different stabilization targets and to the likelihood of staying below certain equilibrium warming levels. For example, respecting constraints of 2°C above pre-industrial levels, at equilibrium, is already outside the range of scenarios considered in this chapter, if the higher values of likely climate sensitivity are taken into account (red curve in Figure 3.38), whilst a constraint of respecting 3°C above pre-industrial levels implies the most stringent of the category I scenarios, with emissions peaking in no more than the next 10 years, again if the higher likely values of climate sensitivity are taken into account. Using the ‘best estimate’ of climate sensitivity (i.e. the estimated mode) as a guide for establishing targets, implies the need for less stringent emission constraints. This ‘best estimate’ assumption shows that the most stringent (category I) scenarios could limit global mean temperature increases to 2°C–2.4°C above pre-industrial levels, at equilibrium, requiring emissions to peak within 10 years. Similarly, limiting temperature increases to 2°C above pre-industrial levels can only be reached at the lowest end of the concentration interval found in the scenarios of category I (i.e. about 450 ppmv CO₂-eq using ‘best estimate’ assumptions). By comparison, using the same ‘best estimate’ assumptions, category II scenarios could limit the increase to 2.8°C–3.2°C above pre-industrial levels at equilibrium, requiring emissions to peak within the next 25 years, whilst category IV scenarios could limit the increase to 3.2°C–4°C above pre-industrial at equilibrium requiring emissions to peak within the next 55 years. Note that Table 3.10 category IV scenarios could result in temperature increases as high as 6.1°C above pre-industrial levels, when the likely range for the value of climate sensitivity is taken into account. Hence, setting policy on the basis of a ‘best estimate’ climate sensitivity accepts a significant risk of exceeding the temperature thresholds, since the climate sensitivity could be higher than the best estimate.

Errata

Figure 3.38: Relationship between global mean equilibrium temperature change and stabilization concentration of greenhouse gases Errata using: (i) ‘best estimate’ climate sensitivity of 3°C (black), (ii) upper boundary of likely range of climate sensitivity of 4.5°C (red), (iii) lower boundary of likely range of climate sensitivity of 2°C (blue) (see also Table 3.9).

Notes:

1. IPCC (2007a) finds that the climate sensitivity is likely to be in the range 2°C –4.5°C, with a ‘best estimate’ of about 3°C, very unlikely to be less than 1.5°C and values substantially higher than 4.5°C ‘cannot be excluded’ (IPCC (2007a, SPM).

2. The simple relationship T_eq = T_2xCO2 x ln([CO₂]/280)/ln(2) is used (see Meehl et al. (2007), Section 10.7, and Table 10.8), with upper and lower values of T_2xCO2 of 2 and 4.5°C.

3. Non-linearities in the feedbacks (including e.g. ice cover and carbon cycle) may cause time dependence of the effective climate sensitivity, as well as leading to larger uncertainties for greater warming levels. This likelihood level is consistent with the IPCC Working Group I assessment of climate sensitivity, see Note 1, and drawn from additional consideration of Box 10.2, Figure 2, in IPCC (2007a).

Table 3.10: Properties of emissions pathways for alternative ranges of CO2 and CO2-eq stabilization targets. Post-TAR stabilization scenarios in the scenario database (see also Sections 3.2 and 3.3); data source: after Nakicenovic et al., 2006 and Hanaoka et al., 2006) Errata

Notes:

a. Warming for each stabilization class is calculated based on the variation of climate sensitivity between 2ºC –4.5ºC, which corresponds to the likely range of climate sensitivity as defined by Meehl et al. (2007,Chapter 10).

b. Ranges correspond to the 70% percentile of the post-TAR scenario distribution.

c. ‘Best estimate’ refers to the most likely value of climate sensitivity, i.e. the mode (see Meehl et al. (2007,Chapter 10) and Table 3.9

Table 3.11 highlights a number of climate change impacts and key vulnerabilities organized as a function of global mean temperature rise (IPCC, 2007b, Chapter 19). The table highlights a selection of key vulnerabilities representative of categories covered in Chapter 19 (Table 19.1) in IPCC (2007b). The italic text in Table 3.11 highlights examples of avoided impacts derived from ensuring that temperatures are constrained to any particular temperature range compared to a higher one. For example, significant benefits result from constraining temperature change to not more than 1.6°C–2.6°C above pre-industrial levels. These benefits would include lowering (with different levels of confidence) the risk of: widespread deglaciation of the Greenland Ice Sheet; avoiding large-scale transformation of ecosystems and degradation of coral reefs; preventing terrestrial vegetation becoming a carbon source; constraining species extinction to between 10–40%; preserving many unique habitats (see IPCC, 2007b, Chapter 4, Table 4.1 and Figure 4.5) including much of the Arctic; reducing increases in flooding, drought, and fire; reducing water quality declines, and preventing global net declines in food production. Other benefits of this constraint, not shown in the Table 3.11, include reducing the risks of extreme weather events, and of at least partial deglaciation of the West Antarctic Ice Sheet (WAIS), see also IPCC, 2007b, Section 19.3.7. By comparison, for ‘best guess’ climate sensitivity, attaining these benefits becomes unlikely if emission reductions are postponed beyond the next 15 years to a time period between the next 15–55 years. Such postponement also results in increasing risks of a breakdown of the Meridional Overturning Circulation (IPCC, 2007b, Table 19.1).

Even for a 2.6°C –3.6°C temperature rise above pre-industrial levels there is also medium confidence in net negative impacts in many developed countries (IPCC, 2007b, Section 19.3.7). For emission-reduction scenarios resulting in likely temperature increases in excess of 3.6°C above pre-industrial levels, successively more severe impacts result. Low temperature constraints are necessary to avoid significant increases in the impacts in less developed regions of the world and in polar regions, since many market sectors in developing countries are already affected below 2.6°C above pre-industrial levels (IPCC, 2007b, Section 19.3.7), and indigenous populations in high latitude areas already face significant adverse impacts.

It is possible to use stablization metrics (i.e. global mean temperature increase, concentrations in ppmv CO₂-eq or radiative forcing in W/m2) in combination with the mitigation scenarios literature to assess the cost of alternative mitigation pathways that respect a given equilibrium temperature, key vulnerability (KV) or impact threshold. Whatever the target, both early and delayed-action mitigation pathways are possible, including ‘overshoot’ pathways that temporarily exceed this level. A delayed mitigation response leads to lower discounted costs of mitigation, but accelerates the rate of change and the risk of transiently overshooting pre-determined targets (IPCC, 2007b, Section 19.4.2).

A strict comparison between mitigation scenarios and KVs is not feasible as the KVs in Table 3.11 refer to realized transient temperatures in the 21^st century rather than equilibrium temperatures, but a less rigorous comparison is still useful. Avoidance of many KVs requires temperature change in 2100 to be below 2°C above 1990 levels (or 2.6°C above pre-industrial levels). Using equilibrium temperature as a guide, impacts or KV could be less than expected, for example if impacts do not occur until the 22^nd century, because there is more time for adaptation. Or they might be greater than expected, as temperatures in the 21^st century may transiently overshoot the equilibrium, or stocks at risk (such as human populations) might be larger. Some studies explore the link between transient and equilibrium temperature change for alternative emission pathways (O’Neill and Oppenheimer, 2004; Schneider and Mastrandrea, 2005; Meinshausen, 2006).

It is transient climate change, rather than equilibrium change, that will drive impacts. More research is required to address the question of emission pathways and transient climate changes and their links to impacts.^[25] In the meantime, equilibrium temperature change may be interpreted as a gross indicator of change, and given the caveats above, as a rough guide for policymakers’ consideration of KV and mitigation options to avoid KV.