Figure 2.6. AOGCM projections of seasonal changes in (a) mean temperature (previous page) and (b) precipitation up to the end of the 21st century for 32 world regions. For each region two ranges between minimum and maximum are shown. Red bar: range from 15 recent AOGCM simulations for the A2 emissions scenario (data analysed for Christensen et al., 2007a). Blue bar: range from 7 pre-TAR AOGCMs for the A2 emissions scenario (Ruosteenoja et al., 2003). Seasons: DJF (December-February); MAM (March-May); JJA (June-August); SON (September-November). Regional definitions, plotted on the ECHAM4 model grid (resolution 2.8 x 2.8°), are shown on the inset map (Ruosteenoja et al., 2003). Pre-TAR changes were originally computed for 1961-1990 to 2070-2099 and recent changes for 1979-1998 to 2079-2098, and are converted here to rates per century for comparison; 95% confidence limits on modelled 30-year natural variability are also shown based on millennial AOGCM control simulations with HadCM3 (mauve) and CGCM2 (green) for constant forcing (Ruosteenoja et al., 2003). Numbers on precipitation plots show the number of recent A2 runs giving negative/positive precipitation change. Percentage changes for the SAH region (Sahara) exceed 100% in JJA and SON due to low present-day precipitation. Key for (a) and (b):
— range of changes from seven pre-TAR AOGCMs for the A2 emissions scenario
— range of changes from 15 recent AOGCM simulations for the A2 emissions scenario
^m 95% confidence limits on modelled 30-year natural variability based on HadCM3 millennial control simulation 95% confidence limits on modelled 30-year natural variability based on CGCM2 millennial control simulation commonly consider only large-scale, period-averaged climate), requiring scenario analysis to be carried out offline. Interpretation of impacts then becomes problematic, requiring a method of relating the large-scale climate change represented in the IAM to the impacts of associated changes in weather extremes modelled offline. Goodess et al. suggest that a more direct, but untested, approach could be to construct conditional damage functions (cdfs), by identifying the statistical relationships between the extreme events themselves (causing damage) and large-scale predictor variables. Box 2.4 offers a global overview of observed and projected changes in extreme weather events.
Projections of atmospheric composition account for the concurrent effects of air pollution and climate change, which can be important for human health, agriculture and ecosystems. Scenarios of CO2 concentration ([CO2]) are needed in some CCIAV studies, as elevated [CO2] can affect the acidity of the oceans (IPCC, 2007; Chapter 6, Section 6.3.2) and both the growth and water use of many terrestrial plants (Chapter 4, Section 4.4.1; Chapter 5, Section 5.4.1), with possible feedbacks on regional hydrology (Gedney et al., 2006). CO2 is well mixed in the atmosphere, so concentrations at a single observing site will usually suffice to represent global conditions. Observed [CO2] in
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