Co-developing seamless climate information for the wine sector

The wine industry is among the agri-food sectors most strongly influenced by climate variability and climate change across multiple time scales. In particular, the integration of reliable and timely sub-seasonal, seasonal, and decadal climate information into decision-making processes can support the wine sector in better managing climate-related risks, such as spring frost events or water-use restrictions. This has led to growing interest in climate information at these different temporal scales.

Despite this interest, several challenges continue to limit the uptake of climate information by users, including the coarse spatial resolution of climate model outputs and the lack of coherence between climate predictions from different forecast systems operating at different time scales. To address these limitations and produce coherent regional climate information tailored to the wine sector, the suitability of various statistical downscaling methods has been assessed to enhance the spatial resolution of user-relevant climate variables and indicators at specific locations in Catalonia.

In addition, a novel methodology for the temporal merging of seasonal and decadal predictions has been developed to improve the accuracy and consistency of key climate variables. This approach has also been explored for sub-seasonal and seasonal predictions, contributing to a more seamless climate information framework.

This new scientific knowledge has been developed within the EU-funded ASPECT project (Adaptation-oriented Seamless Predictions of European ClimaTe) and the SINFONIA Marie Skłodowska-Curie Postdoctoral Fellowship (Towards Seamless climate INFOrmation: merging sub-seasonal and seasonal predictioNs to better manage climate-related rIsks Affecting the wine sector). In both initiatives, the scientific methods are co-developed in close collaboration with representative stakeholders. Building on these interactions, a climate bulletin has been designed to support key vineyard management decisions by integrating seasonal and decadal predictions, with future versions incorporating seamless and higher-resolution climate information as results become available.