A1b A2 A1fi

Figure 2.8. Projected ranges of global mean annual temperature change during the 21st century for CO2-stabilisation scenarios (upper panel, based on the TAR) and for the six illustrative SRES scenarios (middle and lower panels, based on the WG I Fourth Assessment). Different approaches have been used to obtain the estimates shown in the three panels, which are not therefore directly comparable. Upper panel. Projections for four CO2-stabilisation profiles using a simple climate model (SCM) tuned to seven AOGCMs (IPCC, 2001c, Figure SPM-6; IPCC, 2001a, Figure 9.17). Broken bars indicate the projected mean (tick mark) and range of warming across the AOGCM tunings by the 2020s (brown), 2050s (blue) and 2080s (orange) relative to 1990. Time periods are based on calculations for 2025, 2055 and 2085. Approximate CO2-equivalent values - including non-CO2 greenhouse gases - at the time of CO2-stabilisation (ppm) are also shown. Middle panel. Best estimates (red dots) and likely range (red bars) of warming by 2090-2099 relative to 1980-1999 for all six illustrative SRES scenarios and best estimates (coloured dots) for SRES B1, A1B and A2 by 2020-2029, 2050-2059 and 2080-2089 (IPCC, 2007, Figure SPM.5). Lower panel. Estimates based on an SCM tuned to 19 AOGCMs for 2025 (representing the 2020s), 2055 (2050s) and 2085 (2080s). Coloured dots represent the mean for the 19 model tunings and medium carbon cycle feedback settings. Coloured bars depict the range between estimates calculated assuming low carbon cycle feedbacks (mean - 1 SD) and those assuming high carbon cycle feedbacks (mean + 1 SD), approximating the range reported by Friedlingstein et al., 2006. Note that the ensemble average of the tuned versions of the SCM gives about 10% greater warming over the 21st century than the mean of the corresponding AOGCMs. (Meehl et al., 2007, Figure 10.26 and Appendix 10.A.1). To express temperature changes relative to 1850-1899, add 0.5°C.

11 Best estimate and likely range of equilibrium warming for seven levels of CO2-equivalent stabilisation: 350 ppm, 1.0°C [0.6-1.4]; 450 ppm, 2.1°C [1.4-3.1]; 550 ppm, 2.9°C [1.9-4.4]; 650 ppm, 3.6°C [2.4-5.5]; 750 ppm, 4.3°C [2.8-6.4]; 1,000 ppm, 5.5°C [3.7-8.3] and 1,200 ppm, 6.3°C [4.2-9.4] (Meehl et al., 2007, Table 10.8).

Table 2.4. The six SRES illustrative scenarios and the stabilisation scenarios (parts per million CO2) they most resemble (based on Swart et al., 2002).

Climate change and impact outcomes have been identified based on criteria for dangerous interference with the climate system (Mastrandrea and Schneider, 2004; O'Neill and Oppenheimer, 2004; Wigley, 2004; Harvey, 2007) or on meta-analysis of the literature (Hitz and Smith, 2004). A limitation of these types of analyses is that they are not based on consistent assumptions about socio-economic conditions, adaptation and sectoral interactions, and regional climate change.

A third approach constructs a single set of scenario assumptions by drawing on information from a variety of different sources. For example, one set of analyses combines climate change projections from the HadCM2 model based on the S750 and S550 CO2-stabilisation scenarios with socioeconomic information from the IS92a reference scenario in order to assess coastal flooding and loss of coastal wetlands from long-term sea level rise (Nicholls, 2004; Hall et al., 2005) and to estimate global impacts on natural vegetation, water resources, crop yield and food security, and malaria (Parry et al., 2001; Arnell et al., 2002). Scenario integration

The widespread adoption of SRES-based scenarios in studies described in this report (see Boxes 2.2 to 2.7) acknowledges the desirability of seeking consistent scenario application across different studies and regions. For instance, SRES-based downscaled socio-economic projections were used in conjunction with SRES-derived climate scenarios in a set of global impact studies (Arnell et al., 2004; see Section At a regional scale, multiple scenarios for the main global change drivers (socio-economic factors, atmospheric CO2 concentration, climate factors, land use, and technology), were developed for Europe, based on interpretations of the global IPCC SRES storylines (Schröter et al., 2005b; see Box 2.7).

Nationally, scenarios of socio-economic development (Kaivo-oja et al., 2004), climate (Jylhä et al., 2004), sea level (Johansson et al., 2004), surface ozone exposure (Laurila et al., 2004), and sulphur and nitrogen deposition (Syri et al., 2004) were developed for Finland. Although the SRES driving factors were used as an integrating framework, consistency between scenario types could only be ensured by regional modelling, as simple downscaling from the global scenarios ignored important regional dependencies (e.g., between climate and air pollution and between air pressure and sea level: see Carter et al., 2004). Similar exercises have also been conducted in the east (Lorenzoni et al., 2000) and north-west (Holman et al., 2005b) of England.

Integration across scales was emphasised in the scenarios developed for the Millennium Ecosystem Assessment (MA), carried out between 2001 and 2005 to assess the consequences of ecosystem change for human well-being (Millennium Ecosystem Assessment, 2005). An SAS approach (see Section 2.4.5) was followed in developing scenarios at scales ranging from regional through national, basin, and local (Lebel et al., 2005). Many differed greatly from the set of global MA scenarios that were also constructed (Alcamo et al., 2005). This is due, in part, to different stakeholders being involved in the development of scenarios at each scale, but also reflects an absence of feedbacks from the subglobal to global scales (Lebel et al., 2005).

2.4.7 Large-scale singularities

Large-scale singularities are extreme, sometimes irreversible, changes in the Earth system such as abrupt cessation of the Atlantic Meridional Overturning Circulation (MOC) or melting of ice sheets in Greenland or West Antarctica (see Meehl et al., 2007; Randall et al., 2007; also Chapter 19, Section 19.3.5). With few exceptions, such events are not taken into account in socio-economic assessments of climate change. Shutdown of the MOC is simulated in Earth system models of intermediate complexity subject to large, rapid forcing (Meehl et al., 2007; also Chapter 19, Section Artificial 'hosing' experiments, assuming the injection of large amounts of freshwater into the oceans at high latitudes, also have been conducted using AOGCMs (e.g., Vellinga and Wood, 2002; Wood et al., 2003) to induce an MOC shutdown. Substantial reduction of greenhouse warming occurs in the Northern Hemisphere, with a net cooling occurring mostly in the North Atlantic region (Wood et al., 2003). Such scenarios have subsequently been applied in impact studies (Higgins and Vellinga, 2004; Higgins and Schneider, 2005; also see Chapter 19, Section

Complete deglaciation of Greenland and the West Antarctica Ice Sheet (WAIS) would raise sea level by 7 m and about 5 m, respectively (Meehl et al., 2007; also Chapter 19, Section One recent study assumed an extreme rate of sea level rise, 5 m by 2100 (Nicholls et al., 2005), to test the limits of adaptation and decision-making (Dawson et al., 2005; Tol et al., 2006). A second study employed a scenario of rapid sea level rise of 2.2 m by 2100 by adding an ice sheet contribution to the highest TAR projection for the period, with the increase continuing unabated after 2100 (Arnell et al., 2005). Both studies describe the potential impacts of such a scenario in Europe, based on expert assessments.

2.4.8 Probabilistic futures

Since the TAR, many studies have produced probabilistic representations of future climate change and socio-economic conditions suitable for use in impact assessment. The choices faced in these studies include which components of socioeconomic and climate change models to treat probabilistically and how to define the input probability density functions (pdfs) for each component. Integrated approaches derive pdfs of climate change from input pdfs for emissions and for key

Table 2.4. The six SRES illustrative scenarios and the stabilisation scenarios (parts per million CO2) they most resemble (based on Swart et al., 2002).

SRES illustrative

Description of

Surrogate stabilisation




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