Application of high-resolution climate measurement and modelling to the adaptation of New Zealand vineyard regions to climate variability

Initial results are presented of research into the relationship between climate variability and viticulture in New Zealand vineyards. Atmospheric modelling and analytical tools are being developed to improve adaptation of viticultural practices and grape varieties to current and future climate. The research involves application of advanced local and regional scale weather and climate models, and their integration with grapevine phenological and crop models. The key aims are to improve adaptation of grape varieties to fine scale spatial variations of climate, and reduce the impact of climate variation and risk factors such as frost, cool spells and high temperatures. Improved optimization of wine-grape production through better knowledge of climate at high resolution within vineyard regions will contribute to the future sustainability of high quality wine production. An enhanced network of automatic weather stations (AWS) has been installed in New Zealand’s premier vineyard region (Marlborough) and the Weather Research and Forecasting (WRF) model has been set up to run twice daily at 1 km resolution through the growing season. Model performance has been assessed using AWS data and the model output is being used to derive high-resolution maps and graphs of bioclimatic indices for the vineyard region. Initial assessment of model performance suggested that WRF had a cold bias, but this was found to be due to errors in the default surface characteristics. Spatial patterns of predicted air temperature and bioclimatic indices appear to accurately represent the significant spatial variability caused by the complex terrain of the Marlborough region. An automated web page is being developed to provide wine-producers with daily up-dates of observed and modelled information for the vineyard region. Latest results of this research will be provided along with a review of the 2013-14 growing season, using data from both the climate station network and WRF model output.