ClimateChange Assessment Guide.pdf - University of Waterloo

Guide for Assessment of Hydrologic Effects of Climate Change in Ontarioviiithe future with climate change, is crucial to the validityof this approach. Changes in the variability of weatherin the future and weather extremes are addressed bythis method. Predictors generated by GCMs for futuretime periods reflect future climate variability and thusinfluence the variability of the predictands through theregressions. Guidance is provided on acquiring andapplying the Statistical Downscaling Model (SDSM) togenerate future climates that incorporate changes inclimate variability.Weather generators are statistically-based climatemodels that replicate the statistical attributes (i.e.,mean and variance) of local observed climate variables(Christensen et al., 2007a; Wilby et al., 2004). Weathergenerators do not replicate observed sequences ofevents; they simulate the sequence of wet and dryweather events and the transition from one to another(de Loë and Berg, 2006). Often secondary variables suchas air temperature and solar radiation are modelledconditionally on precipitation conditions, especially thepresence or absence of rain. Since weather generatorsrely on climate statistics, long observation records arerequired to develop robust scenarios. Variability ofclimate is addressed with this method as GCM simulatedfuture climate variance can be incorporated in the futureweather model formulation. The weather generatorLARS-WG is discussed in the Guide with information onacquiring and applying the model.Dynamical downscaling using regional climate models(RCMs) is one of the most promising methods fordownscaling GCM output. RCMs are physically based3-D models capable of representing important physicalfeatures (i.e., topography and water bodies) and climaticprocesses such as the formation of convective stormsand soil moisture storage over small portions of theglobe. RCMs are typically nested within GCMs anduse one-way boundary conditions such as sea-surfacetemperatures and atmospheric circulation patterns fromthe GCMs to drive them (de Loë and Berg, 2006). RCMssimulate smaller scale physical processes involvingthe use of interactive land-surface models. Output isavailable at finer resolution than from GCMs, typicallyin the range of 40 to 50 km spacing; thus, an orderof magnitude more spatially detailed than the GCMsoutput. Because of the smaller scale of the modeldomain, RCMs can simulate individual storms (notsmaller convective events such as thunderstorms) and,therefore, can be used to examine the occurrence offuture storms and extreme events.The following table describes these approaches forgenerating future climates and lists their relativeadvantages and disadvantages.Summary GCM-change field and downscaled climates for use in climate change impact assessmentsTechnique Description Advantages DisadvantagesGCM-change fieldSummary GCM continued next page• Simulated climate at coarsegrid spacing• Use to develop monthly(seasonal or other) changefields• Provides data used instatistical downscaling• Wide variety of GCMsavailable• Several GCMs have beenapplied to long-termsimulations for family ofemission scenarios• Comprehensive physicallybased tools• Several variables available• Good agreement acrossGCMs on temperaturechange• Coarse spatial resolution,unrepresentative of localclimate• Extreme events generallyoccur at a smaller scale thanGCM scales, not represented• Modest agreement acrossGCMs on precipitation• Cannot be used to changeclimate variability