Box 27 SRESbased landuse and landcover characterisations

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Future land use was estimated by most of the lAMs used to characterise the SRES storylines, but estimates for any one storyline are model-dependent, and therefore vary widely. For example, under the B2 storyline, the change in the global area of grassland between 1990 and 2050 varies between -49 and +628 million ha (Mha), with the marker scenario giving a change of +167 Mha (Nakicenovic et al., 2000). The IAM used to characterise the A2 marker scenario did not include landcover change, so changes under the A1 scenario were assumed to apply also to A2. Given the differences in socio-economic drivers between A1 and A2 that can affect land-use change, this assumption is not appropriate. Nor do the SRES land-cover scenarios include the effect of climate change on future land cover. This lack of internal consistency will especially affect the representation of agricultural land use, where changes in crop productivity play an important role (Ewert et al., 2005; Audsley et al., 2006). A proportional approach to downscaling the SRES land-cover scenarios has been applied to global ecosystem modelling (Arnell et al., 2004) by assuming uniform rates of change everywhere within an SRES macro-region. In practice, however, land-cover change is likely to be greatest where population and population growth rates are greatest. A mismatch was also found in some of the SRES storylines, and for some regions, between recent trends and projected trends for cropland and forestry (Arnell et al., 2004).

More sophisticated downscaling of the SRES scenarios has been undertaken at the regional scale within Europe (Kankaanpää and Carter, 2004; Ewert et al., 2005; Rounsevell et al., 2005, 2006; Abildtrup et al., 2006; Audsley et al., 2006; van Meijl et al., 2006). These analyses highlighted the potential role of non-climate change drivers in future land-use change. Indeed, climate change was shown in many examples to have a negligible effect on land use compared with socio-economic change (Schröter et al., 2005b). Technology, especially as it affects crop yield development, is an important determinant of future agricultural land use (and much more important than climate change), contributing to declines in agricultural areas of both cropland and grassland by as much as 50% by 2080 under the A1FI and A2 scenarios (Rounsevell et al., 2006). Such declines in land use did not occur within the B2 scenario, which assumes more extensive agricultural management, such as 'organic' production systems, or the widespread substitution of agricultural food and fibre production by bioenergy crops. This highlights the role of policy decisions in moderating future land-use change. However, broad-scale changes often belie large potential differences in the spatial distribution of land-use change that can occur at the sub-regional scale (Schröter et al., 2005b; see also Figure 2.7), and these spatial patterns may have greater effects on CCIAV than the overall changes in land-use quantities (Metzger et al., 2006; Reidsma et al., 2006).

□ 0% □01o-2a% □ -SO to -40% □-4010-60% J-6010-50% H-80to-100%

Figure 2.7. Percentage change in cropland area (for food production) by 2080, compared with the baseline in 2000 for the four SRES storylines (A1FI, A2, B1, B2) with climate calculated by the HadCM3 AOGCM. From Schröter et al., 2005b. Reprinted with permission from AAAS.

□ 0% □01o-2a% □ -SO to -40% □-4010-60% J-6010-50% H-80to-100%

Figure 2.7. Percentage change in cropland area (for food production) by 2080, compared with the baseline in 2000 for the four SRES storylines (A1FI, A2, B1, B2) with climate calculated by the HadCM3 AOGCM. From Schröter et al., 2005b. Reprinted with permission from AAAS.

as part of socio-economic scenario development, hence offering the possibility of gauging the effectiveness of adaptation options in comparison to scenarios without adaptation (Holman et al., 2005b).

2.4.6.8 Mitigation/stabilisation scenarios

Mitigation scenarios (also known as climate intervention or climate policy scenarios) are defined in the TAR (Morita et al., 2001), as scenarios that "(1) include explicit policies and/or measures, the primary goal of which is to reduce GHG emissions (e.g., carbon taxes) and/or (2) mention no climate policies and/or measures, but assume temporal changes in GHG emission sources or drivers required to achieve particular climate targets (e.g., GHG emission levels, GHG concentration levels, radiative forcing levels, temperature increase or sea level rise limits)." Stabilisation scenarios are an important subset of inverse mitigation scenarios, describing futures in which emissions reductions are undertaken so that GHG concentrations, radiative forcing, or global average temperature change do not exceed a prescribed limit.

Although a wide variety of mitigation scenarios have been developed, most focus on economic and technological aspects of emissions reductions (see Morita et al., 2001; van Vuuren et al., 2006; Nakicenovic et al., 2007). The lack of detailed climate change projections derived from mitigation scenarios has hindered impact assessment. Simple climate models have been used to explore the implications for global mean temperature (see Box 2.8 and Nakicenovic et al., 2007), but few AOGCM runs have been undertaken (see Meehl et al., 2007, for recent examples), with few direct applications in regional impact assessments (e.g., Parry et al., 2001). An alternative approach uses simple climate model projections of global warming under stabilisation to scale AOGCM patterns of climate change assuming unmitigated emissions, and then uses the resulting scenarios to assess regional impacts (e.g., Bakkenes et al., 2006).

The scarcity of regional socio-economic, land-use and other detail commensurate with a mitigated future has also hindered impact assessment (see discussion in Arnell et al., 2002). Alternative approaches include using SRES scenarios as surrogates for some stabilisation scenarios (Swart et al., 2002; see Table 2.4), for example to assess impacts on ecosystems (Leemans and Eickhout, 2004) and coastal regions (Nicholls and Lowe, 2004), demonstrating that socio-economic assumptions are a key determinant of vulnerability. Note that WG I reports AOGCM experiments forced by the SRES A1B and B1 emissions pathways up to 2100 followed by stabilisation of concentrations at roughly 715 and 550 ppm CO2 (equated to 835 and 590 ppm equivalent CO2, accounting for other GHGs: see Meehl et al., 2007).

A second approach associates impacts with particular levels or rates of climate change and may also determine the emissions and concentration paths that would avoid these outcomes.

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