USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE-CHANGE SCENARIOS

Climate change scenarios with a high spatial and temporal resolution a re required in the evaluation of the effects of climate change on agri cultural potential and agricultural risk. Such scenarios should reprod uce changes in mean weather characteristics as well as incorporate the changes in climate variability indicated by the global climate model (GCM) used. Recent work on the sensitivity of crop models and climatic extremes has clearly demonstrated that changes in variability can hav e more profound effects on crop yield and on the probability of extrem e weather events than simple changes in the mean values. The construct ion of climate change scenarios based on spatial regression downscalin g and on the use of a local stochastic weather generator is described. Regression downscaling translated the coarse resolution GCM grid-box predictions of climate change to site-specific values. These values we re then used to perturb the parameters of the stochastic weather gener ator in order to simulate site-specific daily weather data. This appro ach permits the incorporation of changes in the mean and variability o f climate in a consistent and computationally inexpensive way. The sto chastic weather generator used in this study, LARS-WG, has been valida ted across Europe and has been shown to perform well in the simulation of different weather statistics, including those climatic extremes re levant to agriculture. The importance of downscaling and the incorpora tion of climate variability are demonstrated at two European sites whe re climate change scenarios were constructed using the UK Met. Office high resolution GCM equilibrium and transient experiments.