Towards Seamless climate INFOrmation: merging sub-seasonal and seasonal predictioNs to better manage climate-related rIsks Affecting the wine sector

The wine industry is one of the agri-food sectors most highly influenced by climate variability and change at different timescales.

In particular, the integration of reliable and timely sub-seasonal to seasonal (S2S) climate information in decision-making might help the wine sector to better manage climate-related risks. This explains the recent interest of the wine industry in these sources of climate information.

However, one of the challenges that prevent the users from the uptake of climate information is the lack of coherence between the climate predictions from different sources (i.e., forecast systems at sub-seasonal and seasonal time scales).

This project will develop a novel methodology to produce coherent S2S seamless climate information for the wine industry.

This will involve 1) the understanding of the inconsistencies between the climate prediction sources at S2S timescales that prevent the users from a coherent flow of climate information and 2) the implementation of a methodology that merges sub-seasonal and seasonal predictions by using the knowledge from the physical processes relevant for the climate predictability at both timescales.

The new scientific knowledge and methods will be co-developed in collaboration with representative stakeholders.

They will work with the fellow to design the climate information that has actual value for the management of the vineyards, wineries, and the strategic planning for the wine market.

The methods developed in this project could be further exploited to produce seamless S2S predictions for other socio-economic sectors (e.g., agriculture, health, and energy).

This action will be developed at the Barcelona Supercomputing Center (Spain), a well-known institution in the implementation of successful climate services for different socio-economic sectors.

Furthermore, a secondment period is projected at Météo-France (France), which is a producing centre of operational sub-seasonal and seasonal predictions.