ClimateChange Assessment Guide.pdf - University of Waterloo

Guide for Assessment of Hydrologic Effects of Climate Change in Ontario32• Capability to produce long time series, important forextreme event simulations;• High frequency output is possible (i.e., sub-dailyoutput);• Good performance for temperature simulation (i.e.,means and extremes) and precipitation (means only);• User friendly software readily available; and• Model parameters can be related to landscapecharacteristics with interpolation.The weaknesses in weather generators for climatesimulation include the following:• Procedure for adjusting parameters for futureconditions is critical, yet not standardized, and may besomewhat arbitrary;• Adjustments to precipitation parameters can affectother parameters to unanticipated degrees, thereforepossible difficulties exist in adjusting parameters forfuture conditions;• Large amounts of observed data are required;• Some models demonstrate poor skill in simulatingprecipitation variance and extremes;• As statistical downscaling relies upon historicalclimate relationships, feedback from climatesubsystems such as vegetation, or anthropogenicimpacts such as land use change, cannot berepresented; and• Weakness in primary assumptions regarding thecontinuity of the statistical relationships duringclimate change.Weather generator software is readily available. Oneversion, LARS-WG, produces daily precipitation,maximum and minimum air temperature, and solarradiation under current and future conditions for a singlesite (Semenev and Barrow, 2002). This model overcomesa problem experienced with many weather generatorsin that they do not well represent periods of extendeddrought or rainfall. The improved approach, employed inLARS-WG, simulates the sequence of wet and dry daysfirst and then determines other parameters conditionalon the precipitation. The model has been redevelopedsince the early 1990s; in its latest version producessynthetic weather data for a wide range of climates.Future climate change can be incorporated in terms ofchanges in mean values on a monthly basis as informedby the GCM scenario runs. Models of this type do notwell reproduce the annual variability of monthly meansof the variables (Sharif and Burn, 2007).Sharif and Burns (2007) describe a K-nearest neighbourweather generator. This variation improves uponextreme event simulation using a process involvingre-sampling of the historical data with perturbations,while preserving prominent statistical characteristics andinter-station correlations. The advantage of providingmulti-station output for larger watersheds is that asingle site weather generator cannot provide spatiallyconsistent output across larger areas. The model isalso demonstrated to reproduce monthly variations inmeans of daily maximum and minimum air temperatureand precipitation. The model performed very wellin comparisons of simulated and observed data inOntario’s Upper Thames River Watershed.Weather generators are valuable tools to generatingfuture climates when reliable long-term observationalclimate data exists.4.3.3 Dynamical - Regional Climate Models (RCMs)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 and thususe one-way boundary conditions such as sea-surfacetemperatures and atmospheric circulation patterns (fromthe GCM runs) to drive them (de Loë and Berg, 2006).RCMs simulate smaller scale physical processes usinginteractive land-surface models. Output is availableat finer resolution than from GCMs, typically in therange of 40 to 50 km spacing; an order of magnitudemore spatially detailed than the GCMs output. Thisincreased level of discretization allows one to includeadditional land forms not included in GCM models. Forexample, RCM scenarios for Ontario include the GreatLakes while GCM scenarios do not. Because of thesmaller scale of the model domain, RCMs can simulateindividual storms (albeit not smaller convective eventssuch as thunderstorms) and, therefore, could be used toexamine future storm events.The strengths of RCMs are as follows (Bader et al., 2008;Carter et al., 2007; Goodess et al., 2003):