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Guide for Assessment of Hydrologic Effects of Climate Change in Ontario22et al., 2008), or whether the assumption that GCMs thatreplicate current climate well will also perform well inprojecting future climate.GCMs have been one of the most commonly usedresources for developing changed climate input datafor water resource impact assessments. Methods usedrange from those that rely entirely on the GCM tomodify historic time series on a monthly average basis(i.e., change field or delta method) through to those thatuse the GCM output to inform local climate modellingtools/approaches (see Chapter 4). In any case, griddedoutput from GCMs is not suitable for direct use inhydrologic models. The GCM resolution, usually on theorder of several hundred kilometres and poor reliabilityat a single time step at a particular grid point, precludeusing these data directly to represent present-day andfuture climates. The change field method is outlined inSections 4.1 and 6.5.2.“Our understanding of climate is still insufficient tojustify proclaiming any one model “best” or evenshowing metrics of model performance that implyskill in predicting the future. More appropriate in anyassessments focusing on [climate change] adaptationor mitigation strategies is to take into account, in apertinently informed manner, the products of distinctmodels built using different expert judgements atcenters around the world” (Bader et al., 2008 11).Essentially, the current recommendation is that climatechange impact assessment should use as manyscenarios of climate change as possible to cover awide range of potential outcomes; scenarios can bederived from differences in climate model formulationsor differences in emission scenarios that drive theclimate response. This point is crucial to developing anunbiased assessment, understanding the uncertaintiessurrounding future climate estimation and developingadaptation strategies that improve resilience to a widerange of future stresses.3.2 GHG Emission Scenarios“Future greenhouse gas (GHG) emissions are theproduct of very complex dynamic systems, determinedby driving forces such as demographic development,socio-economic development, and technologicalchange. Their future evolution is highly uncertain.Scenarios are alternate images of how the future mightunfold and are an appropriate tool to analyse howdriving forces may influence future emission outcomesand to assess associated uncertainties” (Nakicenovicet al., 2000 3).defEmission scenario • plausible representationof the future development of emissions ofsubstances that are potentially radiatively active(e.g., GHGs, aerosols), based on a coherent andinternally consistent set of assumptions aboutdriving forces (such as demographic and socioeconomicdevelopment, technological change)and their key relationships; concentrationscenarios, derived from emission scenarios, areused as input in a climate model [e.g., GCM] tocompute climate projections (Baede et al., 2007)Early modelling of the sensitivity of the climate systemto increasing GHGs was based on equilibrium doublingor quadrupling of carbon dioxide (2xCO 2, 4xCO 2)experiments. As the climate models developed intothe transient response, coupled atmosphere-oceanGCMs, the emission scenarios also evolved. In 1992,the IPCC developed six alternative emission scenariosthat included GHG increase only and GHG (CO 2) andsulphate aerosols combined (the IS92). The IS92a(“business as usual”) scenario represented a mid-rangescenario; emissions were based on historical increasesuntil 1990 then compounded annually at a rate of 1% peryear (Leggett et al., 1992). Although IS92a was the mostfrequently used emission scenario for climate modelsimulations, the IPCC recommended using all six IS92emission scenarios to capture the range of uncertainty infuture emissions (Barrow, 2002).In 2000, the IPCC Special Report on Emission Scenarios(SRES) published a set of new emission scenarios(Nakicenovic et al., 2000). The SRES scenarios werethe “most comprehensive, ambitious, and carefullydocumented emission scenarios produced to date” andpresented a “substantial advance from prior scenarios”(Parson et al., 2007 34). There were 40 SRES scenariosin total, each covering a range of GHG and sulphuremissions identified in the literature and the SRESscenario database (Nakicenovic et al., 2000). These newscenarios contributed to both the IPCC Third and Fourth
Future Climate Scenarios23Assessment Reports (TAR and AR4, respectively) andwere used in climate impacts and adaptation research(Parson et al., 2007 34). These scenarios currentlyrepresent the best available set of scenarios for climatechange impact assessments.Four narrative storylines were developed to describethe SRES marker scenarios: A1, A2, B1 and B2 (Table3.1). The A1 scenario storyline was further dividedinto alternative development pathways for energytechnology: A1FI (fossil fuel intensive), A1B (balanced)and A1T (predominately non-fossil fuel) (Nakicenovicet al., 2000). Each storyline was based on differentdriving forces of GHG and sulphur emissions (seeFigure 3.3). The storylines represented differentdemographic (population), economic (product, financialand labour markets), social (inequality, poverty), cultural(globalization, information technology, electronic media),environmental, technological and governance conditions(Raskin et al., 2002). The A1 and A2 families emphasizedeconomics while the B1 and B2 families focused onenvironmental sustainability. The A2 and B2 familiesare the most widely used in simulations by the climatemodelling community; however, the IPCC recommendsusing all six of these SRES scenarios (i.e., A1FI, A1B, A1T,A2, B1, B2) in climate model experiments to generateclimate scenarios (IPCC-TGICA et al., 2007).In the IPCC Fourth Assessment Report, a subset ofthree IPCC SRES scenarios was selected from the sixcommonly used marker scenarios for projecting climatechange in the 21st century. This subset included B1, A1Band A2 and represented ‘low’, ‘medium’ and ‘high’ nonmitigationscenarios (Meehl et al., 2007).Table 3.1 Characteristics of the SRES storylines (Carter et al., 2007; Nakicenovic et al., 2000)Driving Factor StorylineA1 (A1FI, A1B, A1T) A2 B1 B2World Market-orientated Differentiated Convergent Local solutionsEconomyVery rapid growth; GDP$550 trillion by 2100Fastest per capitagrowthReduction in regionaldifferencesSlow, fragmented,even; GDP $250 trillionby 2100Lowest per capitagrowthRegionally orientatedRapid but lower thanA1; GDP $350 trillionUS by 2100Service, informationbasedReduction in materialintensityIntermediate economicgrowth; GDP $250trillion US by 2100PopulationGovernanceTechnologyEmissionsLow increasePeaks 2050, thendeclines (8.7 billion by2050, 7 billion by 2100)Strong regionalinteractionsIncome convergenceCapacity buildingRapid introduction ofnew, more efficienttechnology; adaptationAbundant energy andmineral resourcesHigh CO 2range3 sub-scenariosbased on alternativetechnological change:A1F1 (intensive fossilfuels), A1B (balanced),A1T (non-fossil fuels)High increaseContinuously increases(15 billion by 2100)Regional diversitySelf-reliancePreservation of localidentitiesSlowest, mostfragmentedMedium-high CO 2rangeLow, same as A1Peaks 2050, thendeclines (8.7 billion by2050, 7 billion by 2100)Global solutionsEconomic, social,environmentalsustainabilityClean, resourceefficientModerate increaseContinuous but lowerthan A2 (10.4 billion by2100)Local solutionsSocial equityEnvironmentalprotectionMore rapid than A2Less rapid, morediverse than A1/B1Low CO 2range Low-medium CO 2range
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