The effect of climate on Burgundy vintage quality rankings

Abstract

Aim: Based on consensus rankings from prominent rating authorities, we examined the importance of a suite of climatic variables, organized by winegrape phenological stage, in distinguishing between high- and low-ranked vintages in Burgundy.Methods and Results: Vintage ratings of Burgundy wines acquired from 12 sources were evaluated to develop consensus rankings for red and white wines from 1961–2015. Climate variables (air temperature, precipitation, degree-day accumulations, etc.) were organized by mean phenological stage and compared between good and poor vintages using Mann-Whitney U tests and multivariate stepwise discriminant function analysis. High temperatures, particularly during the growing season, were found to be the most consistently important climatic factor in distinguishing good-quality vintages from poor-quality vintages. The best red vintages had a greater diurnal temperature range during the growing season, whereas the top white vintages were not distinguished by unusually warm conditions, but the bottom-ranked white vintages were particularly cool and wet. The impact of rainfall varied across the growing season, with top-ranked Burgundy wines benefitting from rainfall during the bud break period and dry conditions during the ripening phase.Conclusions: The most important climatic factor in distinguishing between top- and bottom-ranked vintages is growing season temperature, especially high diurnal temperature range (for reds) and high average maximum temperatures (for whites). Good Burgundy vintages are more likely when there is ample rainfall during the bud break period in April and dry conditions during the véraison and ripening phases.Significance and Impact of the Study: As viticulturalists adapt to regional climate trends, a better understanding of how specific climate variables affect wine quality becomes increasingly important in viticulture management.

Modelling Chablis vintage quality in response to inter-annual variation in weather

“…In our multi-factor model, temperature (Tmean Apr-Sep ) was the best weather factor explaining inter-annual variation in Chablis vintage score. This is consistent with studies on Chardonnay in Burgundy (Outreville, 2018;Davis et al, 2019), in other French regions such as Bordeaux (Baciocco et al, 2014;Ashenfelter, 2017), Rhone (Ashenfelter, 2017) and the Loire Valley (Neethling et al, 2012) with different cultivars, and regions outside of France such as Barolo in Italy and Barossa in Australia (Ashenfelter, 2017).…”

“…The data were also used to generate climatic indices that are typically used for viticulture, including indices for growing season temperature and precipitation for the phenological phases important for wine quality. These included mean Growing Season Temperature (GST) (Jones et al, 2005), the Cool Night Index (CNI) which in the Northern Hemisphere is the mean minimum temperature for September (Tonietto and Carbonneau, 2004), and precipitation during veraison and/or ripening (Ashenfelter, 2010;Baciocco et al, 2014;Davis et al, 2019).…”

“…Mean temperature between May and July is particularly important for the subsequent quality of Chablis wine (Figure 3, Table 2). Flowering and fruitset typically occur in the first half of June for Burgundy as a whole (Davis et al, 2019), though in Bordeaux, for example, can vary by around one month (Jones and Davis, 2000).…”

“…Little research, to our knowledge, has focused specifically on the weather and the wines of Chablis in France. Chablis has been included implicitly, however, within more general studies of Burgundy (Outreville, 2018;Davis et al, 2019). Chablis wines deserve particular attention.…”

Big climate data assessment of viticultural conditions for wine quality determination in France

“…Wine vintage ratings are commonly used to reflect the quality of wine that is produced each year (Jones et al, 2005;van Leeuwen and Darriet, 2016;Davis et al, 2019). Even though each rating system is graded by the different expert(s) using different value ranges (5-, 10-, 20-or 100-point scale), a general agreement between them has been proven (Van Jones, 1998).…”