1. Study area

The Chablis wine region is located in the department of Yonne, in the northern part of Burgundy, France (Figure 1). The vineyards are within a relatively compact area (approximately 16 km (North–South) by 18 km (East–West) centred around the town of Chablis (latitude 47°48′49″ N, longitude: 3°47′54″ E, 140 metres above sea level). The topography is hilly, rising to around 320 metres and the vineyards lie on both sides of the river Serein which runs broadly North–South through the town. Chablis wines are divided into four appellations d'origine contrôlée (AOC). In decreasing order of quality recognition, these are Grand Cru Chablis, Premier Cru Chablis, Chablis and Petit Chablis. For the purposes of this study, all four AOCs are included in the term “Chablis wine”.

Figure 1. Study area: a) location of Chablis, a town in the Yonne, department of the Burgundy region, within France; b) close-up of the Yonne and Côte-d’Or departments.

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Vineyard areas in orange (European Environment Agency, 2020), study area weather stations (WS, lilac circles), and grid square (dashed square) used for climate projections (Drias, 2021).

2. Model development

3. Predicting the quality of Chablis wine in 2041–2070

Climate projections for Chablis were taken from the French Ministère de la Transition Écologique’s Drias les futures du climat service (Drias, 2021) for the grid square centred at latitude 47°48’27” N and longitude 3°46’42” E, approximately 1.8 km from the Chablis weather station. Data were extracted for the RCP (Representative Concentration Pathway) 2.6, 4.5 and 8.5 scenarios at the 5th, 50th (median) and 95th percentiles, using their multi-model approach, for changes in the following variables to the period 2041 to 2070: monthly mean temperature (°C), mean minimum temperature for September (°C) (CNI) and monthly total precipitation (mm). The reference period for these projections is 1976 to 2005, for which data was taken from the Chablis weather station. These figures were then used in conjunction with the Chablis vintage model to predict Chablis wine quality in 2041–2070. This text presents only the median percentile projections. A more complete table, including the 5th and 95th percentile projections, is available in the Supplementary Information (Table S2).

An alternative projection for CNI in 2041–2070 (CNI2) was calculated as mean CNI for the base period (1976 to 2005) plus 40 % of the projected change in GST. This is because the assumption that CNI will rise as much as the same as GST (Drias, 2021) is not supported by the recent past. Mean minimum temperature in September has so far risen far less than GST has for Chablis and nearby regions; 38 % in Chablis (1963 to 2000), 65 % in Cote de Beaune (1961 to 2020, Savigny-lès-Beaune weather station) and 39 % in the Loire Valley (1960 to 2010, using data from Neethling et al., 2012).

4. Tools

R and R Studio (version 1.3.1093, www.r-project.org / www.rstudio.com) were used for statistical analysis. Boxplots and histograms showed that the vintage score and the key climate indices for Chablis were sufficiently normal in distribution for parametric statistical analysis.

1. Warming and vintages of Chablis typicity

The vintage score increased significantly by around 2 points to 7.1 between 1963 and 2018 in Chablis (Table 1).

Several weather variables (sunshine and most temperature indices) but not all (indeed, none of the precipitation indices) also showed significant trends (Table 1). Mean spring/summer temperature (Tmean_Apr-Sep) increased by almost 0.5 °C per decade between 1963 and 2020 and slightly more so than mean autumn/winter temperature cumulatively (Tmean_Apr-Sep 2.68 °C versus Tmean_Oct-Mar 2.16 °C). The mean maximum rose considerably more than the mean minimum temperature (2.33 °C versus 1.03 °C for September). Similarly, the number of days reaching or exceeding 35 °C between 1st April and 30 September each year has risen from close to zero to almost six days; much of this occurred between 2015 and 2020 with 12, 5, 6, 7, 10 and 8 days in successive years, indicating a non-linear trend. Mirroring the above, the number of days where minimum air temperature fell below 0 °C between April and September more than halved.

Sunshine duration also increased, but this linear relationship was not as strong as for most temperature indices. No significant regression was detected for continentality, the difference in mean temperature between the warmest and coldest months (Skelton, 2007).

The regression line fitted over the study period (solid black line, Figure 2) explained over half of the variance in mean spring/summer temperature (Table 1). During this period, years have differed considerably in vintage rating for Chablis wine, but no Poor-quality vintages have been recorded since 1991 (Figure 2). Comparing temperature-year regressions amongst the three vintage classifications showed significant differences (P < 0.005): Excellent vintage rating years provided the shallowest slope, crossing the all-years temperature trend line in 1998. Hence overall, the post-2000 period provided a greater proportion of years with Good Chablis vintages than the 1963–1980 period (Figure 2).

Figure 2. The trend of mean temperature from 1st April to 30 September (TmeanApr-Sep) in Chablis, France, from 1963 to 2018 (solid black line, Adjusted R-squared 0.56, P < 0.001).

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Also shown are the years, and the respective regression of mean temperature against these years only, in which the Chablis wine vintage was rated as Excellent (purple line, Adjusted R-squared 0.44, P = 0.003), Good (green line, Adjusted R-squared 0.51, P < 0.001), or Poor (red line, Adjusted R-squared 0.37, P = 0.04).

2. Vintage quality in response to weather

2.1. Temperature, precipitation and period

Of the eight Chablis weather variables evaluated for this study, mean temperature accounted for the most variance amongst vintage scores. A linear regression with a second-order polynomial for the period May to July (Tmean_May-Jul) explained 43 % of the variance in the vintage score (Table 2). This was the highest adjusted R-squared achieved for any single-factor model and was superior to the April to September (adjusted R-squared 0.364, P < 0.001) and April to October (adjusted R-squared 0.321, P < 0.001) periods for mean temperature.

Precipitation and sunshine duration played lesser roles in the models of the vintage score; August to September was the most important period. Total August and September precipitation in a single-factor model explained around 12 to 16 % of variance (linear or plus a second-order polynomial, P = 0.006, 0.004, respectively), whereas sunshine duration for these months in a single-factor model explained around 12 to 20 % of the variance (linear or plus a second-order polynomial, P = 0.004, 0.001, respectively). Despite the above, the August to September period of sunshine duration and precipitation (nor each month alone) did not retain significance in multiple regression models including temperature as a factor.

The importance of the May to July period was clarified by comparing vintage ratings against both temperature and rainfall for different periods within spring and summer (Figure 3). In these co-plots for temperature and rainfall in March (Figure 3a) or April (Figure 3b), there is no discrimination for the vintage rating with the Poor, Good and Excellent vintage ratings randomly distributed. However, as the periods examined progressed from April to May (Figure 3c) through April to June (Figure 3d), April to July (Figure 3e), until May to July (Figure 3f) the Poor vintage ratings separated towards the bottom of the chart. The majority of Poor vintages occurred when the mean temperature for May to July was below 15.5 °C (Figure 3f); the only two vintages rated higher below 15.5 °C were when total precipitation for the May to July period was less than 150 mm. These co-plots, however, showed little separation between the Good and Excellent vintage ratings.

Figure 3. Mean temperature versus total precipitation for March (a), April (b), April–May (c), April–June (d), April–July (e) and May–July (f), and Chablis vintage rating.

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The final rating classification for each vintage is marked as follows: Purple circle = Excellent vintage (>8 score), green triangle = Good vintage (6–8 score) and red diamond = Poor vintage (<6 score).

Table 2. Parameters of the multiple linear regression Chablis vintage model selected that combines linear and quadratic temperature terms (mean temperature from 1st April to 30 September (TmeanApr-Sep, °C), the Cool Night Index (CNI; mean minimum temperature for September, °C), and total precipitation from 1st June to 30 September (PJun-Sep, mm) to quantify the variation amongst 56 Chablis vintage scores from 1963 to 2018. For comparison, the best single-factor model to quantify variation in the same vintage scores is also provided (linear and quadratic temperature terms for mean temperature from 1st May to 31 July, TmeanMay-Jul). The factor values provide the range of weather variables across which the model was fitted. The normality and homoscedasticity tests are the Shapiro–Wilk and Breusch–Pagan tests, respectively.

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	Mean of Factor Value	Range in Factor Value	Coefficient	Standard Error	Model / Factor P	Model / Cumulative Adj. R2	Normality Test (P)	Homoscedasticity Test (P)
Chablis vintage model					< 0.001	0.571	0.322	0.148
Intercept			–170.90	27.05	< 0.001
TmeanApr-Sep	15.81 °C	13.58–18.30 °C	22.380	3.442	< 0.001	0.172
TmeanApr-Sep2	15.81 °C	13.58–18.30 °C	–0.6790	0.1080	< 0.001	0.364
CNI	9.42 °C	5.80–12.80 °C	–0.4089	0.1109	< 0.001	0.510
PJun-Sep	236.6 mm	119.1–354.2 mm	–0.006918	0.002392	< 0.01	0.571
Single-factor model					< 0.001	0.425	0.118	0.219
Intercept			–129.13239	27.09245	< 0.001
TmeanMay-Jul	16.77 °C	14.63–19.43 °C	15.61145	3.21993	< 0.001	0.204
TmeanMay-Jul2	16.77 °C	14.63–19.43 °C	–0.44424	0.09537	< 0.001	0.425

2.2. The Chablis vintage model

The best model identified and selected for the Chablis vintage model was:

(Equation 1) Vintage Score =

22.38 Tmean_Apr-Sep – 0.6790 Tmean_Apr-Sep² – 0.4089 CNI – 0.006918 P_Jun-Sep – 170.9

where Tmean_Apr-Sep is mean temperature (°C) from 1st April to 30 September, CNI is the Cool Night Index (°C) and P_Jun-Sep is the total precipitation (mm) from 1st June to 30 September. The model and each term were significant and explained 57 % of the variance (Table 2) with a good fit to observations from Poor to Excellent scores (Figure 4).

Figure 4. Comparison of actual Chablis vintage scores from 1963 to 2018 (●) with fitted values from the multiple regression model (Table 2). The line shown indicates perfect agreement.

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The relationship between vintage score and mean temperature (Tmean_Apr-Sep) was curvilinear, described well by linear and quadratic terms, with an optimum for Chablis vintage score at c. 16.5 °C for Equation 1 (Figure 5).

Figure 5. Vintage score vs. TmeanApr-Sep for Chablis vintages from 1963 to 2018 (●). The grey band around the fitted regression line (in blue, Equation 1) represents the standard error.

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Whereas Tmean_May-Jul was the best period for temperature if the fitted model comprised a single weather factor alone, in the multiple regression model with other factors (CNI and P_Jun-Sep) included, Tmean_Apr-Sep was superior (adjusted R-squared increased from 0.509 to 0.571).

Replacing CNI with mean minimum temperature for August and September (Tmin_Aug-Sep) increased the adjusted R-squared marginally (from 0.571 to 0.584), but CNI was retained as it is a recognised climate index for viticulture.

Replacing June to September precipitation (P_Jun-Sep) with the August to September period (P_Aug-Sep) reduced model fit slightly (from 0.571 to 0.533) with the latter period not providing a significant term in the model (P > 0.05).

Replacing the regression equation with an early harvest version to the end of August (Tmean_Apr-Aug, Tmin_Aug and P_Jun-Aug) and an advanced phenology version that brings all the factor periods forward by one month (Tmean_Mar-Aug, Tmin_Aug and P_May-Aug) reduced adjusted R-squared, from 0.571 to 0.415 and 0.346, respectively. Model performance deteriorated further for these early harvest and advanced phenology versions when applied to only the most recent 25, 20, 15 or 10 vintages with a maximum 0.290 R-squared, maximum 0.037 adjusted R-squared and P > 0.10.

Comparison of contour plots from the Chablis vintage model with the actual vintage ratings from 1963 to 2018 (Figure 6) identified the following boundaries of weather indices in relation to the observed vintage ratings. In Figure 6a, the red dashed line at an angle at 14–15 °C denotes the boundary between Poor (on left) and Good or Excellent vintages (on right), whilst the horizontal purple dashed line (at 10.75 °C) denotes the upper CNI limit to Excellent vintages. In Figure 6b, the purple square contains all Excellent vintages bar one (2018), whilst the red broken line denotes the threshold of acceptability below which the cool wet weather almost always provided Poor vintages.

Figure 6. Chablis vintage ratings from 1963 to 2018.

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Purple circle = Excellent vintage (>8 score), green triangle = Good vintage (6–8 score) and red diamond = Poor vintage (< 6 score), compared with the scores from the Chablis vintage model fitted (contour lines) in relation to weather indices: a, CNI vs. Tmean_Apr-Sep where P_Jun-Sep held constant at the long-term mean (236.6 mm); b, Tmean_Apr-Sep vs. P_Jun-Sep where CNI held constant at the long-term mean (9.4 °C). The broken coloured lines are described in the text.

3. Medium-term projections for Chablis wine quality

In the intermediate RCP 4.5 emissions scenario for the period 2041–70, the Chablis vintage model (Equation 1) implies that a Good score would only just be achieved, markedly lower than either the 2009–2018 period or the 1976–2005 base period (Table 4). The RCP 2.6 emissions scenario provided Chablis vintage scores that are similar to the 2009–2018 period and 1976–2005 base period.

1. Chablis wine has generally benefitted from climate change to date

Chablis wine quality has improved during the study period, with no Poor vintages (score < 6) since 1991 (Figure 2), whilst summer temperature and sunshine duration have progressively increased with fewer frost days but no linear trend for precipitation (Table 1). This has gone hand-in-hand with a 73 % increase in vineyard area and, based on the 5-year moving average, an 86 % increase in wine production (BIVB, data by personal communication). The Chablis vintage score model (Equation 1) shows a clear historic link between recent climate change in the region, particularly warmer growing season temperature (GST; Figure 2), and better vintage scores (Figure 4). In addition to this benefit from climate change, Chablis vintage score is also likely to have risen through better vitivinicultural practices such as superior hygiene and frost protection since the 1960s (George, 2007). The effect of temperature on the vintage score quantified here (Table 2) will, in part, also have captured the improvement over time in vitivinicultural practices because of the warming over this period (Figure 2).

Providing GST is below the maximum of a cultivar's range (see below), warmer GST results in greater photosynthetic production of carbohydrates improving flowering and fruitset (Atkinson, 2011) with more reliable ripening (Jones et al., 2005). In addition, phenology is advanced so that veraison and ripening occur earlier in the year when temperatures are warmer (van Leeuwen and Darriet, 2016; Leolini et al., 2018). Earlier budburst does imply closer coincidence with more frost-prone months and so has the potential to reduce yield (Leolini et al., 2018), as occurred throughout Europe, and specifically France, in Spring 2021.

The quadratic relationship detected between Chablis vintage score and Tmean_Apr-Sep (Figure 5), a pattern observed for wines in other regions (Jones et al., 2005), indicates that Chablis quality may not necessarily continue to improve with further warming. The peak was at 16.5 °C (Figure 5). The 2009–2018 mean value for Tmean_Apr-Sep (16.8 °C, Table 4) is at the warm end of the optimum plateau for the vintage score evident in Figure 5, and so further warming may reduce quality. This comment is reinforced by the different regressions of temperature with the year for each rating category where the combined and Good quality relations are almost identical whereas that for Excellent quality is shallower in slope (Figure 2); extrapolation of that relationship into the future, though unwise, would suggest a lower probability of achieving an Excellent vintage of Chablis typicity.

2. GST is the most important factor for vintage quality

The use of monthly weather data precluded the generation of certain temperature-based indices which accumulate above daily temperature thresholds (typically 10 °C in grape) such as Growing Degree Days (GDD), Biologically Effective Degree Days (BEDD), Huglin's Heliothermal Index (HI), or the Winkler Index (WI, based on GDD). This is, however, not considered an issue; mean Growing Season Temperature (GST) as used here and advocated by Jones et al. (2005) and widely used is highly correlated with the other indices for measuring growing season warmth (Moral et al., 2016) and is functionally no different to GDD (Anderson et al., 2012).

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).

It is well established that sugar concentration in grape berries is positively correlated with GST (van Leeuwen and Darriet, 2016); as is the case for Chardonnay (Gambetta et al., 2016). Sugar concentration determines the potential alcohol content of the wine and whilst it is a prerequisite for high quality, excess heat can ultimately lead to over-alcoholic wines which, if accompanied by low concentration of organic acids, are “unbalanced” (Jones et al., 2005; Neethling et al., 2012; van Leeuwen and Destrac-Irvine, 2017). The reasons for greater sugar content at warmer temperatures may involve water loss from berries due to evaporation and greater concentration of the sugar (Pastore et al., 2017) and/or physiological changes that may be genetically controlled and therefore cultivar specific (Suter et al., 2021). Moreover, the composition of secondary metabolites that are responsible for the organoleptic properties of wine, and so affect quality, is changed in grapes ripened at high temperature (van Leeuwen and Destrac-Irvine, 2017).

3. Low peri-flowering period temperature is an early predictor of a Poor vintage

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).

The polynomial relation for May to July mean temperature provided the best single-factor model for the vintage score (Table 2). A threshold of 15.5 °C for this period may provide a simple, easily-applied rule of thumb in advance of harvest to predict vintage quality; below this value vintage quality is likely to be Poor (83 % probability, Figure 3f). Warmth in this period is important for wine quality in other regions also (Real et al., 2017). If it is too cool the flowering period is prolonged and can lead to uneven berry ripeness and wines with vegetal characteristics (Atkinson, 2011), or variable berry size within a bunch (“Millerandage”) and/or berries that are incompletely fertilized (“Shot” berries) which hinder the production of a balanced wine (Gray and Coombe, 2009). Conversely, too high a temperature during flowering can cause premature veraison, inactivation of enzymes and incomplete biosynthesis of compounds associated with flavour (Jones et al., 2005).

4. CNI is the second most important factor in the Chablis vintage model

The Cool Night Index (CNI) was the second most important weather variable, after GST (Tmean_Apr-Sep), in the Chablis vintage model (Table 2). This term in the model accounts for the effect of cool temperature during the ripening period in the 30 days until harvest (Tonietto and Carbonneau, 2004). Whilst warmth in the day is crucial for berry ripening, cool temperatures during the night result in the secondary metabolites associated with high-quality flavours and aromas (Tonietto and Carbonneau, 2004). Moreover, recent research by Aoki et al. (2021), albeit on an indigenous Japanese grapevine variety (V. vinifera cv. Koshu), suggests high night temperatures may promote downy mildew. This may also be relevant because Chardonnay vines are susceptible to downy mildew infection which can taint the wine (Skelton, 2020).

High night temperatures also increase respiration and the degradation of malic acid (Arrizabalaga-Arriazu et al., 2020); and so cool nights preserve acidity in the berries. Acidity is an important aspect of Chablis typicity. Malic acid (usually converted to the smoother-tasting lactic acid by winemakers via malolactic fermentation) typically provides half of the total acidity of grapes and wine with tartaric acid (the other major acid) less prone to degradation during ripening (Jackson, 2014). Wines that lack malic acid (or lactic acid after conversion) may taste flat and are prone to microbial spoilage, although in excess wines can taste sour (Jackson, 2014).

It has been suggested that it is the difference in daily temperature range during ripening, rather than minimum temperature, produces important flavour and aroma compounds (Gladstones, 1992 cited by Jones et al., 2005). However, in developing the model we found that CNI (i.e., mean minimum temperature in September) explained more variance in the vintage score and also the effect of the range between mean minimum and mean maximum temperatures for September was not significant (P > 0.10). Chablis vintage quality is more closely associated with acidity than secondary metabolites, and so the effect on night respiration rates may explain why mean minimum temperature, not the range, in September is an important factor for Chablis.

5. Precipitation from flowering to harvest is negatively related to vintage quality

Every 100 mm increase in P_Jun-Sep reduced the vintage score by almost 0.7 in the model (Table 2). Moderate water stress is also associated with higher quality wine (van Leeuwen et al., 2009; Fraga et al., 2013; Alem et al., 2019). Water stress increases the biosynthesis of secondary metabolites important to the development of aroma in wine, such as the carotenoid-derived C₁₃-norisoprenoids which are associated with floral and fruity odours (Alem et al., 2019). Too much rain between flowering and ripening is also well known to reduce wine quality (Jones and Davis, 2000). This is because berries swell producing lower quality wine (Jones and Davis, 2000; Baciocco et al., 2014) due to a reduction in the concentration of flavour- and aroma-related metabolites (VanderWeide et al., 2021). In cooler climates, high rainfall may lead to sour rot infection, which increases the concentration of acetic acid in berries imparting an unwanted vinegar flavour to the wine (VanderWeide et al., 2021). In this study, Poor vintages of Chablis with high P_Jun-Sep (approximately > 275 mm) were associated with cool Tmean_Apr-Sep, suggesting sour rot may have been an issue in these vintages.

We also compared a model with August to September precipitation (P_Aug-Sep) rather than June to September (P_Jun-Sep). The shorter period was assessed because research for other wine regions emphasised the importance of rainfall in the few weeks leading to harvest (Ashenfelter and Byron, 1995; Davis et al., 2019). In our assessment, however, the use of (P_Aug-Sep) resulted in a non-significant (P = 0.0642) rainfall term. This comparison suggests that the Chablis vintage score is reduced more consistently by greater rainfall over a longer period than just the last weeks before harvest.

6. CNI is a key differentiator between the Chablis vintage model and others for French wines

Several models of the influence of inter-annual variation in weather on wine quality in France have been devised since Ashenfelter, Ashmore and Lalonde (1993, cited by Ashenfelter and Byron, 1995) showed that much of the variability in the price of a Bordeaux vintage could be explained by its age, GST (growing season temperature, April to September), and rainfall both in August and September and also in the prior autumn and winter. Models which represent both the positive effect of temperature during the growing season and the negative effect of rainfall typically account for 35 to 60 % of the variance in the vintage score (Outreville, 2018) or can accurately categorise the top and bottom vintage scores (Baciocco et al., 2014; Davis et al., 2019). The Chablis vintage score model presented here (Table 2) is consistent with the above.

Focusing on Burgundy, Outreville (2018) found that temperature in July and August provided the best (positive) association and rainfall in August and September the best (negative) association with white wine quality. Davis et al. (2019) found that the impact of rainfall on white Burgundy wine quality varied with phenology, but the Chablis vintage model developed here, with monthly data, detected only the negative relation for June to September rainfall reported in Table 2.

The main difference between our Chablis vintage model and one for white Burgundy (Davis et al., 2019) is the inclusion of CNI, which we found to be a more important factor than rainfall.

This difference may be because acidity is more important for Chablis than other white wines from Burgundy (George, 2007). This would tally with the observation that while GST is around 1 °C lower in Chablis compared to Savigny-lès-Beaune, CNI is proportionally far cooler during the ripening period (Table 5) – important to the acid content of the berries. Moreover, minerality, another characteristic of Chablis wine, may be positively correlated with acidity (Ballester et al., 2013). Hence, CNI could be a key variable in the difference in typicities between Chablis and the white wines of the Côte de Beaune, and elsewhere.

7. Model factor periods are affected by advancing phenology

The mean minimum temperature in September (CNI) was an inappropriate factor for the 2003 vintage because the harvest started on 25 August (George, 2007). This was also partly the case for the 2007, 2011, 2015, 2017 and 2018 vintages for which harvest began in early September (Biss, 2020). In both cases, since harvest typically takes around two weeks to complete, August or mid-August to mid-September might be a more relevant period to consider for the CNI in future developments of the model, as suggested by the slight improvement in model performance achieved by using August and September instead of just September. Omazić et al. (2020) found the same problem with CNI for wine regions in Croatia due to earlier harvests.

A similar issue arises for GST and precipitation from flowering to harvest, with climate change advancing the beginning and endpoints for these indices. However, no model improvement was observed by advancing these weather factor periods by one month for the most recent 25, 20, 15 or 10 vintages. Indeed, the advancement to phenology versions of the model performed poorly, but this may have been because there were no Poor-quality wines in these recent periods (Figure 2) and so a narrower range of vintage scores available for analysis. A larger model incorporating the effects of weather on the timing of crop development, soil water balance (such as the Dryness Index (Tonietto and Carbonneau, 2004)), or one with greater temporal resolution and more precise data for wine quality, might be an improvement. All models are simplifications of the real world, however, and our current model has the virtues of simplicity and ease of application.

8. Model limitations

We have developed, and validated, a simple model here to describe the historic effect of inter-annual variation in weather on Chablis vintage score quality. Bonada and Sadras (2015) point out that such approaches are “bound to be inconclusive” because numerous factors are either confounded or analysed insufficiently. Such factors include wind, previous years' weather and vine development, water deficit, vapour pressure deficit, viticultural and oenological practices, and extreme weather events. Neither has the increase in atmospheric CO₂ concentration since 1963 been accounted for. This may affect wine quality with, for example, faster berry development leading to reduced malic and tartaric acid concentrations (Leibar et al., 2017; Arrizabalaga-Arriazu et al., 2020).

There is also the subjective basis of vintage scores (Hodgson, 2008; Cicchetti and Cicchetti, 2013; Jackson, 2014). Other issues include variation in phenology from year to year and the limitation of weather data from only one station given that inter- and intra-vineyard variation in temperature and bioclimatic indices can be as great as at larger scales (Bonnefoy et al., 2013). The above may help to explain why the Chablis vintage model accounted for only 57 % of the variance in the vintage score. We also acknowledge that the model was far better in distinguishing between the Poor and the two better vintages than between the Good and the Excellent (Figures 4 and 6). Nonetheless, the model provides a first approximation of the weather typically associated with high-quality Chablis wine production, compares well with similar models for other regions and/or cultivars, and, we argue, has utility in the comparative ease of application to different scenarios.

9. Climate change and Chablis typicity

Poor Chablis vintages appear to have been avoided in recent years due to warming (Figure 2). Looking ahead, the median probability of the intermediate RCP 4.5 scenario in combination with the Chablis vintage model suggests that Chablis producers will struggle to maintain the high quality of their wine by 2041 to 2070 (predicted score 6.3, Table 4). However, if minimum temperatures in September (CNI) continue to rise more slowly than maximum temperatures the decline may be reduced (CNI2 predicted score 6.8, Table 4). On the other hand, warming is likely to hasten ripening (van Leeuwen and Destrac-Irvine, 2017) further into August which would further increase the effective CNI. Representative Concentration Pathway 2.6 is the only scenario that has a chance (> 66 %) of meeting the Paris Agreement (OECD, 2017); we predict that achieving that target would provide Chablis vintage score only marginally below that of recent years (7.4–7.7 versus 7.8 for 2009–2018).

Vintage scores assess whether wines are of typicity. A Good rating, for example, indicates the wine is not an Excellent example of a Chablis but it may well be very pleasurable to drink (Martin, 2020). Similarly, vintages with the same score may not be identical in all regards. The 2003 and 2018 vintages were noteworthy for being unusually hot (Tmean_Apr-Sep > 18 °C) and dry (P_Jun-Sep < 135 mm). Their ratings were Good (score 6.1) and Excellent (score 8.1), respectively, but they were not like typical Chablis wines lacking acidity (Robinson, 2019) and minerality (Martin, 2020). This consideration is relevant to future vintages. Tables 4 and 5 show that the Chablis region’s climate in 2041 to 2070, especially with the RCP 4.5 emissions scenario, may approach that of today’s Côte de Beaune, a region world-renowned for its premium quality Chardonnay wines such as Corton-Charlemagne, Chassagne-Montrachet, Meursault and Puligny-Montrachet. Hence, although climate change may reduce Chablis vintage score in future (Table 4), Chardonnay grapes grown in the Chablis region are likely to continue to produce premium quality wines.

These predictions must be approached with caution. In addition to errors in the Chablis vintage model, there are uncertainties associated with emissions scenarios and climate modelling (Jacob et al., 2014; OECD, 2017), including the possibility of decadal-scale cold waves (Sgubin et al., 2019). It is also the case that short-term extreme weather events, such as hail and intense rain – the frequency and strength of which will increase with climate change (van Leeuwen and Darriet, 2016) – are not accounted for in the above.

Moreover, it is expected that wine producers will adapt to and/or mitigate the effects of climate change through crop management (van Leeuwen et al., 2019; Santos et al., 2020). The Chablis vintage model presented here may support producers in that task. A further aim is for the model to be applied to identify sites with suitable climates for premium white wine from Chardonnay grapevines in emerging cool climate viticulture regions, such as the UK, an approach to site selection advocated by Ashenfelter (2017).