Climate change scenarios with high spatial and temporal resolution for agricultural applications

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Abstract
Scenarios of climate change with high spatial and temporal resolutions are required for the assessment of the impact of such change on agriculture. A method of producing high resolution scenarios based on regression downscaling techniques linked with a stochastic weather generator is described. Regression relationships were initially determined between observed large-scale and site-specific climate. By assuming that these relationships would be valid in a future climate, they were subsequently used to downscale general circulation model (GCM) data. The UK Meteorological Office high resolution GCM transient experiment (UKTR) was used to construct the climate change scenarios. Site-specific, UKTR-derived changes in a number of weather statistics were used to perturb the parameters of the local stochastic weather generator (LARS-WG), which had initially been calibrated using observed daily climate data. LARS-WG was used to simulate the site-specific daily weather data required by crop growth simulation models. This method permits changes to a wider set of climate parameters in the scenario, including variability. Simulated wheat yields were shown to be more sensitive to changes in climate variability than to changes in the mean. Results are presented for two European sites.

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