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Using a convection-permitting climate model to assess wine grape productivity: two case studies in Italy.

Authors :
Massano, Laura T.
Fosser, Giorgia
Gaetani, Marco
Caillaud, Cécile
Source :
Natural Hazards & Earth System Sciences; 2024, Vol. 24 Issue 12, p4293-4315, 23p
Publication Year :
2024

Abstract

The article explores the potential use of climate models to reproduce wine grape productivity at a local scale in Italy. To this end, both single and multiple regression approaches are used to link productivity data provided by two Italian wine consortia with bioclimatic indices. Temperature- and precipitation-based bioclimatic indices are computed using the observational dataset E-OBS, the high-resolution climate reanalysis product SPHERA, the regional climate model CNRM-ALADIN, and the kilometer-scale convection-permitting climate model CNRM-AROME. The multiple regression method outperforms the single regression systematically, enhancing the ability of bioclimatic indices to explain productivity variability. The results show that productivity is strongly tied to temperature-based bioclimatic indices in the area of the Consorzio per la tutela del Franciacorta in northern Italy, while for the Consorzio del Vino Nobile di Montepulciano area in central Italy both temperature- and precipitation-based indices are relevant. Climate models, providing similar results as E-OBS and SPHERA, appear to be a useful tool to explain productivity variance. In particular, the added value of convection-permitting resolution is evident when precipitation-based indices are considered. This assessment shows windows of opportunity for using climate models, especially at a convection-permitting scale, to investigate future climate change impact on wine production. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15618633
Volume :
24
Issue :
12
Database :
Complementary Index
Journal :
Natural Hazards & Earth System Sciences
Publication Type :
Academic Journal
Accession number :
182028278
Full Text :
https://doi.org/10.5194/nhess-24-4293-2024