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Which SRES scenario to select

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Introduction

The selection of which climate change scenarios to use depends to a certain extent upon the application. For example, some impact models, e.g. a crop-growth simulation model, will require scenario information for a number of climate variables. Not all of the models currently available from the CCDS website have a complete climate variable data set and so the experiments which can be used will be limited to those which contain all the variables which are required in the impact model.

The scenario user needs to decide whether IS92 and/or SRES scenarios should be used, whether an IS92a Greenhouse Gas (GG) only scenario will be selected (i.e. the effects of sulphate aerosols are not included in these scenarios) and, if time is limited, which scenarios should be chosen to best reflect the range of changes expected in the area concerned.

Ensemble-mean scenarios (IS92 and SRES)

An ensemble simulation consists of a number of climate change experiments undertaken with identical forcing scenarios, and these have been performed at a number of modelling centres. CCDS has ensemble simulations from the following IS92a and SRES forced GCM experiments:

SAR(1995) TAR(2001) AR4 (2007)
GA SR-A1 SR-A1B
GG SR-A2 SR-A2
SR-B1 SR-B1
SR-B2
SR-A1FI
SR-A1T

The climate system is assumed to respond to the changes in atmospheric composition (modelled through the use of emissions scenarios) with a climate change signal, upon which is superimposed noise due to natural climate variability. An ensemble simulation is undertaken in order to better distinguish the climate change signal from the natural variability of the system. All of the individual experiments represent this climate change signal and the noise due to natural climate variability.

Scenarios including sulphate aerosol effects (IS92 GA and all SRES)

The effects of atmospheric aerosols (derived from fossil fuel combustion and biomass burning) on climate are included in the GA and all SRES GCM experiments. Aerosols affect climate directly by scattering and absorbing solar radiation and indirectly by altering cloud properties and longevity, the net effect being to cool the surface. Most GCM experiments to date include only the direct effect of sulphate aerosols. Although those GCM experiments which account for both the negative forcing associated with historically observed concentrations of aerosols and the positive forcing due to greenhouse gas forcing over the same period (i.e. the warm start transient experiments) have achieved a closer correspondence to the observed global-mean temperature changes over the historical period than those experiments which consider only greenhouse gas forcing (see Figure 1), a number of concerns have been expressed about using GCM experiments which include sulphate aerosol effects, particularly in those experiments using the IS92a emission scenarios.

Global-mean temperature change (°C)

Figure 1:

Global-mean temperature change (°C) with respect to 1961-1990 for observed (black), the three greenhouse gas + aerosol experiments (blue), the ensemble-mean of these three experiments (thicker blue line) and the greenhouse gas only experiment (red) undertaken with the CGCM1 model.

The IS92a sulphate forcing scenario contains large increases in sulphur dioxide (SO2) emissions over this century, with sulphur loadings over south-east Asia being exaggerated and those over North America and Europe being underestimated. More recent estimates of SO2 emissions, for example those associated with the IPCC Special Report on Emissions Scenarios (2000), see only a small rise over the next couple of decades followed by reduction to levels lower than today's by 2100, due to restrictions on SO2 emissions in Europe and North America and due to the rapid uptake of newer and cleaner technologies, particularly in Asia. Also, more recent sulphur cycle models generate a lower sulphate burden per tonne of SO2 emissions and the radiative effect of the sulphate particles in more sophisticated radiation models is smaller than previously calculated. In addition to these concerns, the indirect effects of sulphate aerosols on the reflectivity and longevity of clouds are considered to be at least as important as the direct effects, but have generally not been included in experiments to date and neither have the effects of other types of aerosols (e.g., carbon and soot) which act to warm the atmosphere. Hence, it is likely that the sulphate-aerosol induced patterns of climate change in the GCM experiments using the IS92a emission scenario are quite different from those in GCM experiments using the SRES emissions scenarios.

Given that there is a large amount of uncertainty regarding the future path of both greenhouse gas and sulphate aerosol emissions, scenarios which include IS92a sulphate aerosol emission effects should not be dismissed out of hand because of the above concerns. Instead, they may be considered as part of a comprehensive assessment of the impacts of climate change, in line with the IPCC recommendation that "users should design and apply multiple scenarios in impacts assessments, where these scenarios span a range of possible future climates, rather than designing and applying a single 'best guess' scenario".

How many scenarios should be used? What scenarios should I select?

The above IPCC recommendation that users should be applying multiple scenarios in impacts assessments leads to the question, "How many scenarios should I be using? What type of scenarios should I be considering?" The short answer to these questions is: use as many as you possibly can to best represent the average and/or extremes for the location or region of interest.

At a minimum, you should be using scenarios constructed from two different GCMs, but the use of additional GCMs is also encouraged. If you only have time to use a small number of scenarios, then you should attempt to select scenarios which represent the extreme range of changes projected for the region in question, as well as a scenario which generally reflects the average change.

For example, by plotting mean temperature and precipitation changes for each scenario for the region/model grid box that you require, you can determine which scenarios are associated with the most extreme changes. These plots are known as "scatterplots". Figure 2 below shows a sample scatterplot.

Annual mean temperature change (°C) versus precipitation change (%)

Figure 2:

Annual mean temperature change (°C) versus precipitation change (%) for the grid box in which Regina (50.4°N, 104.7°W) is located; the plot illustrates the range of results from the different GCM experiments for the 2020s. The symbols define the results from the following SRES and IS92a GCM experiments:

You may also be limited to using those scenarios which contain information for all the climate variables that he/she requires. Therefore, the first step in scenario selection is to identify the climate variables that you will need and then determine which scenarios meet your requirements. Once you have done this you can then identify which scenarios represent the extreme and average changes if you don't have time to use all the scenarios meeting your requirements.

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