Estimating disease risk at the whole plant level with General Circulation Models

General Circulation Models (GCMs) have been developed to assess the impacts of potential global climate change. However, these models do not provide specific weather information at the whole-plant level and thus provide only very gross estimates of conditions that affect plant and disease development. Also, climatic change may increase the frequency of extreme events that influence plant production more than changes in daily or monthly averages. One solution is a simulation approach that can scale weather information from the global down to the plant scale. Over the last 4 years, we have been developing methods to hierarchically define current and forecast weather conditions down to the whole-plant level based on nested high-resolution atmospheric (mesoscale) numerical models. Two hierarchical mesoscale model approaches were tested to downscale weather data in a vineyard. The first, known as the Localized Mesoscale Forecast System (LMFS) uses surface databases to 'localize' mesoscale output. The second, known as the Canopy-Mesoscale Forecast System (CMFS), uses a boundary layer model to downscale mesoscale output. To illustrate the utility of this approach we focused on surface wetness duration (SWD), a variable with high spatial and temporal variability. SWD is also a critical variable for prediction of plant disease. Simulations of SWD with on-site input data were compared to those derived from the mesoscale models and to on-site sensors. Forecasts of atmospheric variables by the two systems were compared to on-site observations. Success in this effort leads us to extend this method to GCMs where factors such as temperature, rainfall, relative humidity, and surface wetness can be estimated within plant and crop canopies. We explore the implications of this work on evaluating the assessment of climate change on the risk of plant disease development.