IPCC Report.pdf - Adam Curry

Changes in Climate Extremes and their Impacts on the Natural Physical EnvironmentChapter 3diurnal cycle of convection (Gutowski et al., 2003; Brockhaus et al.,2008; Lenderink and Van Meijgaard, 2008). Development of sub-dailystatistical downscaling methods is constrained by the availability oflong observed time series for calibration and validation and thisapproach is not currently widely used for climate change applications,although some weather generators, for example, do provide hourlyinformation (Maraun et al., 2010).It is not possible in this chapter to provide assessments of projectedchanges in extremes at spatial scales smaller than for large regions(Table 3-3). These large-region projections provide a wider context fornational or more local projections, where they exist, and, where they donot, a first indication of expected changes, their associated uncertainties,and the evidence available. Several countries, for example in Europe,North America, Australia, and some other regions, have developednational or sub-national projections (generally based on dynamicaland/or statistical downscaling), including information about extremes,and a range of other high-resolution information and tools are availablefrom national weather and hydrological services and academic institutionsto assist users and decisionmakers.3.2.3.2. Uncertainty Sources in Climate Change ProjectionsUncertainty in climate change projections arises at each of the stepsinvolved in their preparation: determination of greenhouse gas andaerosol precursor emissions (driven by socioeconomic developmentand represented through the use of multiple emissions scenarios),concentrations of radiatively active species, radiative forcing, and climateresponse including downscaling. Also, uncertainty in the estimation ofthe true ‘signal’ of climate change is introduced by both errors in themodel representation of Earth system processes and by internal climatevariability.As was noted in Section 3.2.3.1, most shortcomings in GCMs andRCMs result from the fact that many important small-scale processes(e.g., representations of clouds, convection, land surface processes) arenot represented explicitly (Randall et al., 2007). Some processes –particularly those involving feedbacks (Section 3.1.4), and this isespecially the case for climate extremes and associated impacts – arestill poorly represented and/or understood (e.g., land-atmosphereinteractions, ocean-atmosphere interactions, stratospheric processes,blocking dynamics) despite some improvements in the simulations ofothers (see Box 3-2 and below). Therefore, limitations in computingpower and in the scientific understanding of some physical processescurrently restrict further global and regional climate model improvements.In addition, uncertainty due to structural or parameter errors in GCMspropagates directly from global model simulations as input to RCMsand thus to downscaled information.These problems limit quantitative assessments of the magnitude andtiming, as well as regional details, of some aspects of projected climatechange. For instance, even atmospheric models with approximately 20-kmhorizontal resolution still do not resolve the atmospheric processessufficiently finely to simulate the high wind speeds and low pressurecenters of the most intense hurricanes (Knutson et al., 2010).Realistically capturing details of such intense hurricanes, such as theinner eyewall structure, would require models with 1-km horizontalresolution, far beyond the capabilities of current GCMs and of mostcurrent RCMs (and even global numerical weather prediction models).Extremes may also be impacted by mesoscale circulations that GCMsand even current RCMs cannot resolve, such as low-level jets and theircoupling with intense precipitation (Anderson et al., 2003; Menendez etal., 2010). Another issue with small-scale processes is the lack of relevantobservations, such as is the case with soil moisture and vegetationprocesses (Section 3.2.1) and relevant parameters (e.g., maps of soil typesand associated properties, see for instance Seneviratne et al., 2006b;Anders and Rockel, 2009).Since many extreme events, such as those associated with precipitation,occur at rather small temporal and spatial scales, where climatesimulation skill is currently limited and local conditions are highlyvariable, projections of future changes cannot always be made with ahigh level of confidence (Easterling et al., 2008). The credibility ofprojections of changes in extremes varies with extreme type, season, andgeographical region (Box 3-2). Confidence and credibility in projectedchanges in extremes increase when the physical mechanisms producingextremes in models are considered reliable, such as increases in specifichumidity in the case of the projected increase in the proportion of summerprecipitation falling as intense events in central Europe (Kendon et al.,2010). The ability of a model to capture the full distribution of variables– not just the mean – together with long-term trends in extremes,implies that some of the processes relevant to a future warming worldmay be captured (Alexander and Arblaster, 2009; van Oldenborgh et al.,2009). It should nonetheless be stressed that physical consistency ofsimulations with observed behavior provides necessary but not sufficientevidence for credible projections (Gutowski et al., 2008a).While downscaling provides more spatial detail (Section 3.2.3.1), theadded value of this step and the reliability of projections always needsto be assessed (Benestad et al., 2007; Laprise et al., 2008). A potentiallimitation and source of uncertainty in downscaling methods is that thecalibration of statistical models and the parameterization schemes usedin dynamical models are necessarily based on present (and past) climate(as well as an understanding of physical processes). Thus they may notbe able to capture changes in extremes that are induced by futuremechanistic changes in regional (or global) climate, that is, if usedoutside the range for which they were designed (Christensen et al.,2007). Spatial inhomogeneity of both land use/land cover and aerosolforcing adds to regional uncertainty. This means that the factors inducinguncertainty in the projections of extremes in different regions maydiffer considerably. Some specific issues inducing uncertainties in RCMprojections are the interactions with the driving GCM, especially interms of biases and climate change signal (e.g., de Elía et al., 2008;Laprise et al., 2008; Kjellström and Lind, 2009; Déqué et al., 2011) andthe choice of regional domain (Wang et al., 2004; Laprise et al., 2008).130