World: market-oriented Economy: fastest per capita growth Population: 2050 peak, then decline Governance: strong regional interactions; income convergence Technology: three scenario groups:
• A1FI: fossil intensive
■ A1T: non-fossil energy sources
• A1B: balanced across all sources
World: convergent Economy: service and information based; lower growth than A1 Population: same as A1 Governance: global solutions to economic, social and environmental sustainability
Technology: clean and resource-efficient
World: differentiated Economy: regionally oriented; lowest per capita growth Population: continuously increasing Governance: self-reliance with preservation of local identities Technology: slowest and most fragmented development
World: local solutions Economy: intermediate growth Population: continuously increasing at lower rate than A2 Governance: local and regional solutions to environmental protection and social equity Technology: more rapid than A2; less rapid, more diverse than A1/B1
Figure 2.5. Summary characteristics of the four SRES storylines (based on Nakicenovic et al., 2000).
SRES presented four narrative storylines, labelled A1, A2, B1, and B2, describing the relationships between the forces driving GHG and aerosol emissions and their evolution during the 21st century for large world regions and globally (Figure 2.5). Each storyline represents different demographic, social, economic, technological, and environmental developments that diverge in increasingly irreversible ways and result in different levels of GHG emissions. The storylines assume that no specific climate policies are implemented, and thus form a baseline against which narratives with specific mitigation and adaptation measures can be compared.
The SRES storylines formed the basis for the development of quantitative scenarios using various numerical models that were presented in the TAR. Emissions scenarios were converted to projections of atmospheric GHG and aerosol concentrations, radiative forcing of the climate, effects on regional climate, and climatic effects on global sea level (IPCC, 2001a). However, little regional detail of these projections and no CCIAV studies that made use of them were available for the TAR. Many CCIAV studies have applied SRES-based scenarios since then, and some of these are described in Boxes 2.3 to 2.7 to illustrate different scenario types.
7 Of 17 chapters surveyed, SRES-based scenarios were used by the majority of impact studies in 5 chapters, and by a large minority in 11 chapters. The most common usage is for climate scenarios, while examples of studies employing SRES-based socio-economic, environmental, or land-use scenarios comprise a small but growing number. The remaining impact studies used either earlier IPCC scenarios (e.g., IS92) or characterisations derived from other sources.
The most recent climate projection methods and results are extensively discussed in the WG I volume (especially Christensen et al., 2007a; Meehl et al., 2007), and most of these were not available to the CCIAV studies assessed in this volume. Box 2.3 compares recent climate projections from Atmosphere-Ocean General Circulation Models (AOGCMs) with the earlier projections relied on throughout this volume. While AOGCMs are the most common source of regional climate scenarios, other methods and tools are also applied in specific CCIAV studies. Numerous regionalisation techniques8 have been employed to obtain high-resolution, SRES-based climate scenarios, nearly always using low-resolution General Circulation Model (GCM) outputs as a starting point. Some of these methods are also used to develop scenarios of extreme weather events.
The development and application of scenarios from highresolution regional climate models and global atmospheric models (time-slices) since the TAR confirms that improved resolution allows a more realistic representation of the response of climate to fine-scale topographic features (e.g., lakes, mountains, coastlines). Impact models will often produce different results utilising high-resolution scenarios compared with direct GCM outputs (e.g., Arnell et al., 2003; Mearns et al., 2003; Stone et al., 2003; Leung et al., 2004; Wood et al., 2004). However, most regional model experiments still rely on only one driving AOGCM and scenarios are usually available from only one or two regional climate models (RCMs).
More elaborate and extensive modelling designs have facilitated the exploration of multiple uncertainties (across different RCMs, AOGCMs, and emissions scenarios) and how those uncertainties affect impacts. The PRUDENCE project in Europe produced multiple RCM simulations based on the ECHAM/OPYC AOGCM and HadAM3H AGCM simulations for two different emissions scenarios (Christensen et al., 2007b). Uncertainties due to the spatial scale of the scenarios and stemming from the application of different RCMs versus different GCMs (including models not used for regionalisation) were elaborated on in a range of impact studies (e.g., Ekstrom et al., 2007; Fronzek and Carter, 2007; Hingray et al., 2007; Graham et al., 2007; Olesen et al., 2007). For example, Olesen et al. (2007) found that the variation in simulated agricultural impacts was smaller across scenarios from RCMs nested in a single GCM than it was across different GCMs or across the different emissions scenarios.
The construction of higher-resolution scenarios (now often finer than 50 km), has encouraged new types of impact studies. For example, studies examining the combined impacts of increased heat stress and air pollution are now more feasible because the resolution of regional climate models is converging with that of air-quality models (e.g., Hogrefe et al., 2004). Furthermore, scenarios developed from RCMs (e.g., UKMO,
2001) are now being used in many more regions of the world, particularly the developing world (e.g., Arnell et al., 2003; Gao et al., 2003; Anyah and Semazzi, 2004; Government of India, 2004; Rupa Kumar et al., 2006). Results of these regional modelling experiments are reported in Christensen et al. (2007a).
Statistical downscaling (SD)
Much additional work has been produced since the TAR using methods of statistical downscaling (SD) for climate scenario generation (Wilby et al., 2004b; also see Christensen et al., 2007a). Various SD techniques have been used in downscaling directly to (physically-based) impacts and to a greater variety of climate variables than previously (e.g., wind speed), including extremes of variables. For example, Wang et al. (2004) and Caires and Sterl (2005) have developed extreme value models for projecting changes in wave height.
While statistical downscaling has mostly been applied for single locations, Hewitson (2003) developed empirical downscaling for point-scale precipitation at numerous sites and on a 0.1°-resolution grid over Africa. Finally, the wider availability of statistical downscaling tools is being reflected in wider application; for example, the Statistical Downscaling Model (SDSM) tool of Wilby et al. (2002), which has been used to produce scenarios for the River Thames basin (Wilby and Harris, 2006). Statistical downscaling does have some limitations; for example, it cannot take account of small-scale processes with strong time-scale dependencies (e.g., land-cover change). See Christensen et al. (2007a) for a complete discussion of the strengths and weaknesses of both statistical and dynamical downscaling.
The improved availability of high-resolution scenarios has facilitated new studies of event-driven impacts (e.g., fire risk -Moriondo et al., 2006; low-temperature impacts on boreal forests - Jonsson et al., 2004). Projected changes in extreme weather events have been related to projected changes in local mean climate, in the hope that robust relationships could allow the prediction of extremes on the basis of changes in mean climate alone. PRUDENCE RCM outputs showed non-linear relationships between mean maximum temperature and indices of drought and heatwave (Good et al., 2006), while changes in maximum 1-day and 5-day precipitation amounts were systematically enhanced relative to changes in seasonal mean precipitation across many regions of Europe (Beniston et al., 2007). In a comprehensive review (citing over 200 papers) of the options available for developing scenarios of weather extremes for use in Integrated Assessment Models (IAMs), Goodess et al. (2003) list the advantages and disadvantages of applying direct GCM outputs, direct RCM outputs, and SD techniques. Streams of daily data are the outputs most commonly used from these sources, and these may pose computational difficulties for assessing impacts in IAMs (which
8 Defined in the TAR as "techniques developed with the goal of enhancing the regional information provided by coupled AOGCMs and providing fine-scale climate information" (Giorgi et al., 2001).
Was this article helpful?