IPCC_Managing Risks of Extreme Events.pdf - Climate Access

Chapter 3Changes in Climate Extremes and their Impacts on the Natural Physical Environmentthe assessment of climate model performance with respect to extremes(summarized in Sections 3.3 to 3.5 for specific extremes), particularly atthe regional scale, is still limited by the rarity of extreme events thatmakes evaluation of model performance less robust than is the case foraverage climate. Evaluation is further hampered by incomplete data onthe historical frequency and severity of extremes, particularly for variablesother than temperature and precipitation, and for specific regions(Section 3.2.1; Table 3-2).The requirement for projections of extreme events has provided one ofthe motivations for the development of regionalization or downscalingtechniques (Carter et al., 2007). These have been specifically developedfor the study of regional- and local-scale climate change, to simulateweather and climate at finer spatial resolutions than is possible withGCMs – a step that is particularly relevant for many extremes giventheir spatial scale. These techniques are, nonetheless, constrained by thereliability of large-scale information coming from GCMs. Recentadvances in downscaling for extremes are discussed below.As indicated in the Glossary, downscaling “is a method that derives localtoregional-scale (up to 100 km) information from larger-scale modelsor data analyses.” Two main methods are distinguished: dynamicaldownscaling and empirical/statistical downscaling (Christensen et al.,2007). The dynamical method uses the output of regional climatemodels (RCMs), global models with variable spatial resolution, or highresolutionglobal models. The empirical/statistical methods developstatistical relationships that link the large-scale atmospheric variableswith local/regional climate variables. In all cases, the quality of thedownscaled product depends on the quality of the driving model.Dynamical and statistical downscaling techniques are briefly introducedhereafter. Specific limitations that need to be considered in the evaluationof projections are also discussed in Section 3.2.3.2.The most common approach to dynamical downscaling uses highresolutionRCMs, currently at scales of 20 to 50 km, but in some casesdown to 10 to 15 km (e.g., Dankers et al., 2007), to represent regionalsub-domains, using either observed (reanalysis) or lower-resolutionGCM data to provide their boundary conditions. Using non-hydrostaticmesoscale models, applications at 1- to 5-km resolution are also possiblefor shorter periods (typically a few months, a few full years at most) – ascale at which clouds and convection can be explicitly resolved and thediurnal cycle tends to be better resolved (e.g., Grell et al., 2000; Hay et al.,2006; Hohenegger et al., 2008; Kanada et al., 2010b). Less commonlyused approaches to dynamical downscaling involve the use ofstretched-grid (variable resolution) models and high-resolution ‘timeslice’models (e.g., Cubasch et al., 1995; Gibelin and Deque, 2003;Coppola and Giorgi, 2005) with the latter including some simulations at20 km globally (Kamiguchi et al., 2006; Kitoh et al., 2009; Kim et al.,2010). The main advantage of dynamical downscaling is its potential forcapturing mesoscale nonlinear effects and providing information formany climate variables at a relatively high spatial resolution, althoughstill not as high as some require. Dynamical downscaling cannot provideinformation at the point (i.e., weather station) scale (a scale at whichthe RCM and GCM parameterizations would not work). Like GCMs,RCMs provide precipitation averaged over a grid cell, which means atendency to more days of light precipitation (Frei et al., 2003; Barring etal., 2006) and reduced magnitude of extremes (Chen and Knutson,2008; Haylock et al., 2008) compared with point values. These scalingissues need to be considered when evaluating the ability of RCMs andGCMs to simulate precipitation and other extremes.Statistical downscaling methods use relationships between large-scalefields (predictors) and local-scale surface variables (predictands) thathave been derived from observed data, and apply these to equivalentlarge-scale fields simulated by climate models (Christensen et al., 2007).They may also include weather generators that provide the basis for anumber of recently developed user tools that can be used to assesschanges in extreme events (Kilsby et al., 2007; Burton et al., 2008; Qian etal., 2008; Semenov, 2008). Statistical downscaling has been demonstratedto have potential in a number of different regions including Europe(e.g., Schmidli et al., 2007), Africa (e.g., Hewitson and Crane, 2006),Australia (e.g., Timbal et al., 2008, 2009), South America (e.g., D’Onofrioet al., 2010) and North America (e.g., Vrac et al., 2007; Dibike et al.,2008). Statistical downscaling methods are able to access finer spatialscales than dynamical methods and can be applied to parameters thatcannot be directly obtained from RCMs. Seasonal indices of extremescan, for example, be simulated directly without having to first producedaily time series (Haylock et al., 2006a), or distribution functions ofextremes can be simulated (Benestad, 2007). However, statisticaldownscaling methods require observational data at the desired scale(e.g., the point or station scale) for a long enough period to allow themodel to be well trained and validated, and in some methods can lackcoherency among multiple climate variables and/or multiple sites. Onespecific disadvantage of some, but not all, methods based on theanalog approach is that they cannot produce extreme events greater inmagnitude than have been observed before (Timbal et al., 2009).Moreover, statistical downscaling does not allow for the possibility offuture process-based changes in relationships between predictors andpredictands (see Section 3.2.3.2). There have been few systematicintercomparisons of dynamical and statistical downscaling approachesfocusing on extremes (Fowler et al., 2007b). Two examples focus onextreme precipitation for the United Kingdom (Haylock et al., 2006a) andthe Alps (Schmidli et al., 2007), respectively. A few hybrid statisticodynamicaldownscaling methods also exist, including a two-stepapproach used to downscale heavy precipitation events in southernFrance (Beaulant et al., 2011). A conceptually similar cascading techniquehas also been used to downscale tropical cyclones (Bender et al., 2010;see Section 3.4.4).In terms of temporal resolution, while GCMs and RCMs operate atsub-daily time steps, model output at six-hourly or shorter temporalresolutions, which is desirable for some applications such as urbandrainage, is less widely available than daily output. Where limitedstudies have been undertaken, there is evidence that at the typicalspatial resolutions used (i.e., non-cloud/convection-resolving scales),RCMs do not adequately represent sub-daily precipitation and the129