IPCC_Managing Risks of Extreme Events.pdf - Climate Access

Case StudiesChapter 9develop in a matter of minutes (in the case of tornadoes), it can beacross seasons and decades that occurrence of extremes can changeclimatically (McBean, 2000). Since planning for hazardous eventsinvolves decisions across a full range of time scales, ‘An Earth-systemPrediction Initiative for the 21st Century’ covering all scales has beenproposed (Shapiro et al., 2007, 2010).With the rapid growth in the number of humanitarian disasters, thedisaster risk management community has become attentive to changesin extreme events possibly attributed to climate change includingfloods, droughts, heat waves, and storms that cause the most frequentand economically damaging disasters (Gall et al., 2009; Munich Re,2010; Vos et al., 2010). Early warning systems provide an adaptationoption to minimize damaging impacts resulting from projected severeevents. Such systems also provide a mechanism to increase publicknowledge and awareness of natural risks and may foster improvedpolicy- and decisionmaking at various levels.Important developments in recent years in the area of sub-seasonal andseasonal-to-interannual prediction have led to significant improvementsin predictions of weather and climate extremes (Nicholls, 2001; Simmonsand Hollingsworth, 2002; Kharin and Zwiers, 2003; Medina-Cetina andNadim, 2008). Some of these improvements, such as the use of soilmoisture initialization for weather and (sub-) seasonal prediction (Kosteret al., 2010), have potential for applications in transitional zones betweenwet and dry climates, and in particular in mid-latitudes (Koster et al.,2004). Such applications may potentially be relevant for projections oftemperature extremes and droughts (Lawrimore et al., 2007; Schubertet al., 2008; Koster et al., 2010). Decadal and longer time scale predictionsare improving and could form the basis for early warning systems in thefuture (Meehl et al., 2007, 2009; Palmer et al., 2008; Shukla et al., 2009,2010).Developing resiliency to weather and climate involves developingresiliency to its variability on a continuum of time scales, and in an idealworld, early warnings would be available across this continuum (Chapters1 and 2; McBean, 2000; Hellmuth et al., 2011). However, investments indeveloping such resiliency are usually primarily informed by informationonly over the expected lifetime of the investment, especially amongpoorer communities. For the decision of which crops to grow next season,some consideration may be given to longer-term strategies but themore pressing concern is likely to be the expected climate over the nextseason. Indeed, there is little point in preparing to survive the impacts ofpossible disasters a century in the future if one is not equipped to survivemore immediate threats. Thus, within the disaster risk managementcommunity, preparedness for climate change must involve preparednessfor climate variability (Chapters 3 and 4).Improving prediction methods remains an active area of research and itis hoped significant further progress will be reached in coming years(Brunet et al., 2010; Shapiro et al., 2010). However for such predictionsto be of use to end users, improved communication will be required todevelop indices appropriate for specific regional impacts. A betterawareness of such issues in the climate modeling community throughgreater feedback from the disaster risk management community (andother user communities) may lead to the development of additionalapplications for weather and climate hazard predictions. Predictionsystems, if carefully targeted and sufficiently accurate, can be usefultools for reducing the risks related to climate and weather extremes(Patt et al., 2005; Goddard et al., 2010).Despite an inevitable focus on shorter-term survival and hence interestin shorter-term hazard warnings, the longer time scales cannot beignored if reliable predictions are to be made. Changing greenhouse gasconcentrations are important even for seasonal forecasting, becauseincluding realistic greenhouse gas concentrations can significantlyimprove forecast skill (Doblas-Reyes et al., 2006; Liniger et al., 2007).Similarly, adaptation tools traditionally based on long-term records(e.g., stream flow measurements over 50 to 100 years) coupled with theassumption that the climate is not changing may lead to incorrectconclusions about the best adaptation strategy to follow (Milly et al.,2008). Thus reliable prediction and successful adaptation both need aperspective that includes consideration of short to long time scales(days to decades).While there are potential benefits of early warning systems (NRC, 2003;Shapiro et al., 2007) that span a continuum of time scales, for much ofthe disaster risk management community the idea of preparednessbased on predictions is a new concept. Most communities have largelyoperated in a reactive mode, either to disasters that have already occurredor in emergency preparedness for an imminent disaster predicted withhigh confidence (Chapter 5). The possibility of using weather and climatepredictions longer than a few days to provide advanced warning ofextreme conditions has only been a recent development (Brunet et al.,2010; Shapiro et al., 2010). Despite over a decade of operational seasonalpredictions in many parts of the globe, examples of the use of suchinformation by the disaster risk management community are scarce, dueto the uncertainty of predictions and comprehension of their implications(Patt et al., 2005; Meinke et al., 2006; Hansen et al., 2011). Mostseasonal rainfall predictions, for example, are presented as probabilitiesthat total rainfall over the coming few (typically three) months will beamongst the highest or lowest third of rainfall totals as measured overa historical period and these are averaged over large areas (typicallytens of thousands of square kilometers). Not only are the probabilitieslacking in precision but the target variable – seasonal rainfall total –does not necessarily map well onto flood occurrence. Although higherthan-normalseasonal rainfall will often be associated with a higher riskof floods, it is possible for the seasonal rainfall total to be unusuallyhigh yet no flooding occurs. Alternatively, the total may be unusuallylow, yet flooding might occur because of the occurrence of an isolatedheavy rainfall event (Chapter 3). Thus even when seasonal predictionsare understood properly, it may not be obvious how to utilize them.These problems emphasize the need for the development of tools totranslate such information into quantities directly relevant to end users.Better communication between modeling centers and end users isneeded (Chapters 5 and 6). Where targeted applications have been518