Projected shifts of wine regions in response to climate
change
M. Moriondoa, G.V. Jonesb, B. Boisc, C. Dibarid, R. Ferrised, G. Trombid, M. Bindid
a CNR-IBIMET, via G. Caproni 8, 50145, Florence, Italy
b Department of Environmental Studies, Southern Oregon University, 97520, 101A Taylor Hall,
Ashland, OR, U.S.A.
c Institute of the Vine and Wine “J. Guyot”, University of Burgundy, 21078, rue Claude Ladrey 1,
DIJON Cedex, France
d Department of Agri-food Production and Environmental Sciences, University of Florence, 50144,
Piazzale delle Cascine 18, Florence, Italy
*Corresp. author: Marco Moriondo, +39.055.32.88.257, +39.055.332.472, Email: marco.moriondo@cnr.it
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Climatic structure of wine regions
The north-south pattern is clearly visible from the analysis of the Principal Component Analysis
(PCA ) outputs performed on the indices averaged over each wine region (all the indices have been
centered and scaled) (Fig. 1). The plot of factor scores of PC1 and PC2 (85% of the entire
variability) shows that the wine regions are spread along the PC1 axis, which is negatively
correlated to all the predictor variables (inset in Fig. 1) with a correlation ranging from r=-0.31 for
T_JAN to r=-0.38 for WIN. Regions with the lowest values of the indices are placed on the extreme
right side of the PC1- axis, which include German (Mosel, Württemberg, Pfalz) and northern
France (Champagne, Bourgogne) wine regions. On the other hand, wine regions with the highest
values of the indices are present on the extreme left side of the graph and include Southern France
(Roussillon), Spanish (La Mancha, Ribera de Guadiana) and Portuguese (Ribatejo) wine regions. In
contrast, no clear trend was observed with the PC2 axis, which is positively correlated to T_JUL,
HI, W_DEF, Win and AVG_T and negatively to T_JAN, CI and BEDD (T_JUL and T_JAN have
the maximum correlation with r=0.58 and r=-0.62, respectively). Along this axis, the regions with
negative values are generally found in wine regions along both the Mediterranean and Atlantic
shores and as a consequence of the moderating effect of the coastal proximity are characterized by
T_JAN>0 and a relatively low T_JUL. For example in Ribatejo and Penedes a relatively higher
T_JAN (7.8°C and 5.1°C) is associated with a relatively lower T_JUL (27.3°C and 27.1°C).
By contrast, positive values of PC2 are evident for those regions that are more continental, such as
Württemberg, Pfalz, Bourgogne or Champagne, which exhibit a T_JAN<0, or La Mancha and
Ribeira de Guadiana which are associated with the highest values in T_JUL (32.8°C and 34.0°C)
(Fig. 1).
This spatial shift of the wine regions was further analyzed by plotting a smoothed-kernel curve
distribution of each region along the climatic indices that had the largest impact on the classification
process (W_DEF, HI, and T_JAN and T_JUL) (Fig. 2). The analysis shows that the response curves
were generally uni-modal and often skewed, spanning from extremely hot and dry to wet and cold
regions. While there are different ranges associated with each wine region, some significant
overlapping curves were found. The results differentiate three broad types of areas: 1) relatively wet
and cold areas including the German regions (Pfalz, Württemberg and Mosel) and the North
Atlantic coastal areas of France; 2) an intermediate area including coastal Mediterranean regions
and 3) extremely dry and hot regions, including inland areas of the Mediterranean region but with
some outliers notably Ribeira de Guadiana for W_DEF and HI and Ribatejo for T_JAN. The
response curves of intermediate regions are in many cases flatter with respect to extreme regions,
indicating a larger variability of these indices over these areas. For instance, the region of
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Württemberg in Germany yields a median value for T_JAN of -2.75 °C with 1st and 3rd quartiles of
-3.34°C and -2.15°C. On the other hand, Provence exhibits an average T_JAN of 2.55°C with 1st
and 3rd quartiles of 0.91°C and 4.18°C. Similar examples are evident also for W_DEF and HI.
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Figure 1. Principal Component Analysis (PCA) performed on the climate indices for each wine region. The inset figure represents the position of the variables in the correlation circle.
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Figure 2 Probability density functions (PDFs) of the most important predictor variables for wine
regions. For clarity of the figure, the results for some wine regions were omitted.
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References
Jones GV, White MA, Cooper OR, Storchmann K (2005a) Climate change and global wine quality. Clim Change 73:
319-343. Doi: 10.1007/s10584-005-4704-2
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