Page - 270 - in Loss and Damage from Climate Change - Concepts, Methods and Policy Options

270 A.Lopezet al. As a result, the uncertainty associated with a complete modelling chain, from climateforcingandsimulationtoestimationofhazardprobability,islikelytoincrease in each step, and becomeparticularly large at local scales. In addition, uncertainty estimates are always conditional on themodelling approachesused toobtain them, anddonotcapturethefulluncertainty(SmithandStern2011;Stainforthetal.2007), especially at local scales, where current modelling tools to generate projections cannotproducereliableandrobustestimatesof futurechanges (Oreskesetal.2010; RisbeyandO’Kane2011).This isparticularly important in thecaseofcatastrophic changes in the climate system that might occur due to non-linear feedbacks and processesthatarenotknown,orhavenotbeenadequatelyincorporatedintheclimate modelsyet. Nonetheless, the presence of uncertainties in the estimation of hazards, and the factthatinsomecasestheseuncertaintiesmightnotdecreaseintime,4shouldnotstop decisions beingmade. In the next sectionwediscuss someof the decision-making approachesutilised forCCAtodeal specificallywith this issue. 11.3 Challenges forL&DDecision-Making L&D—bothas apolitical concept but also in its technical dimension requiresdeci- sionstobemadeatdifferentscalesfromlocal toglobal,andbyarangeofstakehold- erswith differing priorities and agendas. These can be broadly grouped into three categories of L&D decisionmaking goals (Surminski and Lopez 2014): creating awareness about the sensitivity of human and natural systems to climate change; developing risk reduction and riskmanagement approaches to enhance adaptation, reduce vulnerability and build resilience; and informing compensation and burden sharingmechanisms. All threerequireanunderstandingof thecurrentandfuturescaleanddistribution ofclimaterelatedL&D.Asnotedabove,decisionmakersarefacedwithuncertainties related tohazard, exposureandvulnerability:projectionsof futureweatherpatterns fromdifferent climatemodels oftendisagree (Heal andMilner 2014),while socio- economic trends, which influence the impact of climate change, also suffer from inherentuncertainty(IPCC2012).Forsome,thismayproveasapotentiallywelcome excuseforinaction,forothersthismightleadtoheated,almostunresolvabledisputes about theunderlyingscience.Can thispotentialparalysation (Dessai et al. 2009)be avoided? 4For instance, Knutti and Sedlacek (2013) show that the projected global temperature change from the IPCCAR5models is very similar to the one reported by the IPCCAR4models after taking into account the different underlying scenarios. Similarly, spatial patterns of temperature andprecipitationchangeand localmodel spreadarealsoveryconsistentdespite substantialmodel development.These authors argue thatmodel improvementsoften implymoreconfidence in their projections,butdonotnecessarilynarrowuncertainties.