IPCC_Managing Risks of Extreme Events.pdf - Climate Access

Changes in Climate Extremes and their Impacts on the Natural Physical EnvironmentChapter 3are limited in both quantity and quality (Section 3.2.1), resulting inuncertainty in the estimation of past changes; the signal-to-noise ratiomay be low for many variables and insufficient data may be available todetect such weak signals. In addition, global climate models (GCMs)have several issues in simulating extremes and downscaling techniquescan only partly circumvent these issues (Section 3.2.3).Single-step attribution based on optimal detection and attribution (e.g.,Hegerl et al., 2007) can in principle be applied to climate extremes.However, the difference in statistical properties between mean valuesand extremes needs to be carefully addressed (e.g., Zwiers et al., 2011;see also Section 3.1.6). Post-processing of climate model simulations toderive a quantity of interest that is not explicitly simulated by the models,by applying empirical methods or physically based models to the outputsfrom the climate models, may make it possible to directly compareobserved extremes with climate model results. For example, sea levelpressure simulated by multiple GCMs has been used to derivegeostrophic wind to represent atmospheric storminess and to derivesignificant wave height on the oceans for the detection of externalinfluence on trends in atmospheric storminess and northern oceanswave heights (X.L. Wang et al., 2009a). GCM-simulated precipitationand temperature have also been downscaled as input to hydrologicaland snowpack models to infer past and future changes in temperature,timing of the peak flow, and snow water equivalent for the westernUnited States, and this enabled a detection and attribution analysis ofhuman-induced changes in these variables (Barnett et al., 2008).If a single-step attribution of causes to effects on extremes or physicalimpacts of extremes is not feasible, it might be feasible to conduct amultiple-step attribution. The assessment would then need to be basedon evidence not directly derived from model simulations, that is, physicalunderstanding and expert judgment, or their combination. For instance,in the northern high-latitude regions, spring temperature has increased,and the timing of spring peak flows in snowmelt-fed rivers has shiftedtoward earlier dates (Regonda et al., 2005; Knowles et al., 2006). Achange in streamflow may be attributable to external influence ifstreamflow regime change can be attributed to a spring temperatureincrease and if the spring temperature increase can be attributed toexternal forcings (though these changes may not necessarily be linked tochanges in floods; Section 3.5.2). If the chain of processes is established(e.g., in this case additionally supported by the physical understandingthat snow melts earlier as spring temperature increases), the confidencein the overall assessment would be similar to, or weaker than, the lowerconfidence in the two steps in the assessment. In cases where theunderlying physical mechanisms are less certain, such as those linkingtropical cyclones and sea surface temperature (see Section 3.4.4), theconfidence in multi-step attribution can be severely undermined. Anecessary condition for multi-step attribution is to establish the chain ofmechanisms responsible for the specific extremes being considered.Physically based process studies and sensitivity experiments that helpthe physical understanding (e.g., Findell and Delworth, 2005;Seneviratne et al., 2006a; Haarsma et al., 2009) can possibly play a rolein developing such multi-step attributions.Extreme events are rare, which means that there are also few dataavailable to make assessments regarding changes in their frequency orintensity (Section 3.2.1). When a rare and high-impact meteorologicalextreme event occurs, a question that is often posed is whether such anevent is due to anthropogenic influence. Because it is very difficult torule out the occurrence of low-probability events in an unchangedclimate and because the occurrence of such events usually involvesmultiple factors, it is very difficult to attribute an individual event toexternal forcing (Allen, 2003; Hegerl et al., 2007; Dole et al., 2011; seealso FAQ 3.2). However, in this case, it may be possible to estimatethe influence of external forcing on the likelihood of such an eventoccurring (e.g., Stott et al., 2004; Pall et al., 2011; Zwiers et al., 2011).3.2.3. Projected Long-Term Changes and UncertaintiesIn this section we discuss the requirements and methods used forpreparing climate change projections, with a focus on projections ofextremes and the associated uncertainties. The discussion draws on theAR4 (Christensen et al., 2007; Meehl et al., 2007b; Randall et al., 2007)with consideration of some additional issues relevant to projections ofextremes in the context of risk and disaster management. More detailedassessments of projections for specific extremes are provided inSections 3.3 to 3.5. Summaries of these assessments are provided inTable 3-1. Overviews of projected regional changes in temperatureextremes, heavy precipitation, and dryness are provided in Table 3-3(see pages 196-202).3.2.3.1. Information Sources for Climate Change ProjectionsWork on the construction, assessment, and communication of climatechange projections, including regional projections and of extremes,draws on information from four sources: (1) GCMs; (2) downscaling ofGCM simulations; (3) physical understanding of the processes governingregional responses; and (4) recent historical climate change (Christensenet al., 2007; Knutti et al., 2010b). At the time of the AR4, GCMs were themain source of globally available regional information on the range ofpossible future climates including extremes (Christensen et al., 2007).This is still the case for many regions, as can be seen in Table 3-3.The AR4 concluded that statistics of extreme events for present-dayclimate, especially temperature, are generally well simulated by currentGCMs at the global scale (Randall et al., 2007). Precipitation extremesare, however, less well simulated (Randall et al., 2007; Box 3-2). Asthey continue to develop, and their spatial resolution as well as theircomplexity continues to improve, GCMs could become increasingly usefulfor investigating smaller-scale features, including changes in extremeweather events. However, when we wish to project climate and weatherextremes, not all atmospheric phenomena potentially of relevance canbe realistically or explicitly simulated. GCMs include a number ofapproximations, known as parameterizations, of processes (e.g., relatingto clouds) that cannot be fully resolved in climate models. Furthermore,128