Climate projections

Meteosim will be involved in the second part of the tool that consists in delivering the best climate information at multi-decadal time scale. Such information will be needed by the end-users for strategic planning and adaptation decisions, for example giving hints on the potential geographical shift of viticultural regions.

The usual spatial resolution of global climate models at multi-decadal timescale is around 100 km. However, for the wine grape production, application information is required at significantly finer spatial scale. This justifies the choice of using the 12.5 Km-resolution downscaled Euro-Cordex dataset over Europe instead. Such dataset consists of an ensemble of climate simulations based on multiple dynamical and empirical-statistical downscaling models forced by multiple global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The set of simulations with a horizontal resolution of 12.5 Km have emission scenarios RCP4.5 (9-member multi-model ensemble) and RCP8.5 (10-member multi-model ensemble). Empirical-Statistical-Downscaling approaches will be developed over the areas of interest using the existing observations.

In order to produce the decadal climatic prediction for the next 20-30 years’ time-scale considered, a set of historical climate models data will be evaluated against the existent observations and over the areas of interest. The most influent variables in viticulture will be evaluated, such as mean temperature and precipitation. After that, the changes and trends and the corresponding uncertainty in theses variables will be analyzed and reported.

CMIP5 multi-model ensemble mean of projected changes in December, January and February and June, July and August surface air temperature for the period 2016–2035 relative to 1986–2005 under RCP4.5 scenario (left panels). The right panels show an estimate of the model-estimated internal variability (standard deviation of 20-year means). Source: Fifth Assessment Report (AR5) by IPCC 

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 730253.