Int J Biometeorol
DOI 10.1007/s00484-012-0617-8
ORIGINAL PAPER
Future scenarios for viticultural zoning in Europe: ensemble
projections and uncertainties
H. Fraga & A. C. Malheiro & J. Moutinho-Pereira &
J. A. Santos
Received: 26 July 2012 / Revised: 7 November 2012 / Accepted: 2 December 2012
# ISB 2013
Abstract Optimum climate conditions for grapevine
growth are limited geographically and may be further challenged by a changing climate. Due to the importance of the
winemaking sector in Europe, the assessment of future
scenarios for European viticulture is of foremost relevance.
A 16-member ensemble of model transient experiments
(generated by the ENSEMBLES project) under a greenhouse gas emission scenario and for two future periods
(2011–2040 and 2041–2070) is used in assessing climate
change projections for six viticultural zoning indices. After
model data calibration/validation using an observational
gridded daily dataset, changes in their ensemble means
and inter-annual variability are discussed, also taking into
account the model uncertainties. Over southern Europe, the
projected warming combined with severe dryness in the
growing season is expected to have detrimental impacts on
the grapevine development and wine quality, requiring
measures to cope with heat and water stress. Furthermore,
the expected warming and the maintenance of moderately
wet growing seasons over most of the central European
winemaking regions may require a selection of new grapevine varieties, as well as an enhancement of pest/disease
control. New winemaking regions may arise over northern
Europe and high altitude areas, when considering climatic
factors only. An enhanced inter-annual variability is also
projected over most of Europe. All these future changes
pose new challenges for the European winemaking sector.
Electronic supplementary material The online version of this article
(doi:10.1007/s00484-012-0617-8) contains supplementary material,
which is available to authorized users.
H. Fraga (*) : A. C. Malheiro : J. Moutinho-Pereira : J. A. Santos
Centre for the Research and Technology of Agro-Environmental
and Biological Sciences (CITAB), University of Trás-os-Montes e
Alto Douro (UTAD), PO Box 1013, 5001-801 Vila Real, Portugal
e-mail: [email protected]
Keywords European viticultural zoning . Bioclimatic
indices . Climate change . Ensemble projections . Viticulture .
Model uncertainties
Abbreviations
CompI
Composite index
DI
Dryness index
ECA&D European climate assessment and dataset
GCM
Global climate model
GSP
Growing season precipitation
GSS
Growing season suitability
HI
Huglin index
HyI
Hydrothermal index
IPCC
International panel on climate change
MOS
Model output statistics
NIQR
Normalized interquartile range
RCM
Regional climate model
TR
Total range
SRES
Synthesis report on emission scenarios
Introduction
Climate plays a predominant role in grapevine growth (e.g.
van Leeuwen et al. 2004; Santos et al. 2011), as vine physiology and its development phases are determined mostly by
specific environmental conditions (e.g. Magalhães 2008). In
fact, monthly mean temperatures and precipitation totals in the
growing season present significant correlations with grapevine yield in many regions (e.g. Makra et al. 2009; Santos et
al. 2012a).
Grapevine is a heat demanding crop, requiring a 10 °C
basal temperature for its growing cycle onset and development (Amerine and Winkler 1944; Winkler 1974), and
Int J Biometeorol
relatively high solar radiation intensities (e.g. Magalhães
2008). However, prolonged exposure to excessive heat (e.g.
temperatures above 40 °C) may have detrimental impacts on
some physiological processes (Berry and Bjorkman 1980;
Osorio et al. 1995), resulting in poor yields and quality
(Kliewer 1977; Mullins et al. 1992). Although grapevines
are also resistant to relatively low temperatures (lower thermal
lethal limit of approximately −17 °C) during early stages
(Hidalgo 2002), frost occurrences during spring can severely
damage crop production (e.g. Spellman 1999). Further, excessive humidity in spring can trigger pests and diseases, such as
downy mildew (Carbonneau 2003), while severe dryness
during the growing season can also lead to harmful water
stress (Koundouras et al. 1999) thus leading to reductions in
grapevine productivity (Moutinho-Pereira et al. 2004).
Given these considerations, climate change brings new and
major challenges for winegrape growers. In fact, projected
future changes over Europe under the A1B International
Panel on Climate Change (IPCC) – Synthesis Report on
Emission Scenarios (SRES) scenario (Nakićenović et al.
2000) include a temperature increase of 2.3–5.3 °C in northern
Europe and 2.2–5.1 °C in southern Europe by the end of the
twenty-first century (Christensen et al. 2007). In addition, for
the same scenario, it has been shown that temperature
extremes are also expected to increase throughout Europe
(Andrade et al. 2012).
Changes in inter-annual variability and extremes in climatic factors result in shifts in grapevine phenology, disease
and pest patterns, lower predictability and regularity of the
yields and wine quality (Schultz 2000; Jones et al. 2005a),
being thus an additional pitfall for the winemaking sector.
Conversely, benefits coming from the increase in CO2 concentrations under future atmospheric conditions may also
play a key role in grapevine physiology and yield attributes
(Moutinho-Pereira et al. 2009), though this forcing is out of
the scope of the present study.
Taking this climatic forcing into account, current grapevine global geographical distribution is limited largely to
regions where the growing season mean temperature (April–
September in the Northern Hemisphere) is within the 12–
22 °C range (Jones 2006). Taking the aforementioned delicate balance between environmental conditions and viticultural zoning into account, climate change is likely to have
considerable regional impacts not only on grapevine attributes, but also on wine quality (e.g. Jones and Davis 2000;
Jones et al. 2005b). Due to the high socioeconomic relevance of the winemaking sector in many European regions,
the assessment of the impacts of climate change on
viticulture is of the utmost importance, particularly for
the most renowned winemaking regions spread over the
continent. Winegrape growers are indeed becoming increasingly aware of these changes and their resulting
impacts (Battaglini et al. 2009).
Future climatic zones are then expected to shift polewards, leading to changes in the current regions suitable
for grapevine growing and to new potential winemaking
regions. In addition, due to the projected decrease in the
annual precipitations over southern Europe (Christensen et
al. 2007), grapevines are also expected to be affected negatively by severe dryness (Koundouras et al. 1999; Santos et
al. 2003).
Several studies in different areas of research used ensembles of model runs in assessing climate change projections
under anthropogenic radiative forcing. As simple illustrations, Lobell et al. (2006) used a set of regional climate
models to assess the impacts of climate change on perennial
crop yields in California, while Heinrich and Gobiet (2011)
used an 8-member ensemble for future projections of dry/
wet spells in Europe. The use of multi-model ensemble
projections enables quantification of numerical model
uncertainties arising from differences in physics and modelling approaches, model parameterizations and initialisations, among others. This is important because model
uncertainty may lead to different outcomes (Deser et al.
2012). Another advantage of this approach is that multimodel ensembles commonly outperform studies based on
projections from a single model (Knutti et al. 2010). Besides
using statistical methodologies for model validation/calibration, enabling correction of model bias, quantification of the
uncertainty associated to climate change projections may be
as important to the winemaking sector as the climate change
signal itself.
The present study assesses potential changes in the suitability of a given region for winegrape growth in Europe
under human-driven climate change. For this purpose, several bioclimatic indices, specifically developed for viticultural zoning, are applied using datasets from a 16-member
multi-model ensemble under the A1B emission scenario for
2011–2040 and 2041–2070 (future periods). Hence, this
study is structured as follows: (1) a multi-model ensemble
is used for assessing climate change projections in a set of
viticultural bioclimatic indices, (2) a validation with a stateof-the-art observational gridded daily dataset is conducted
and calibration techniques are then applied to the model
outputs, (3) the ensemble projections and the corresponding
model uncertainties are analysed, (4) an analysis based on a
categorized Composite Index (CompI) is carried out, and (5)
the projected changes in the inter-annual variability are
assessed. In the next section, the bioclimatic indices are
defined, model data is presented, model output statistics
(MOS) and model uncertainty are discussed. In the
“Results” section, after an initial model skill intercomparison, the bioclimatic indices are presented, along
with their inter-annual variability. An analysis of each individual model of the 16-member ensemble is also performed,
depicting the cost of using single models versus the use of
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model ensembles. In the final section, the main outcomes of
this study will be summarized and discussed.
Materials and methods
Bioclimatic indices
The implications of climate change on viticultural zoning in
Europe were assessed on the basis of projections for the
following bioclimatic indices: Huglin Index (HI; Huglin
1978), Dryness Index (DI; Riou et al. 1994), Hydrothermal
Index (HyI; Branas et al. 1946), Growing Season Suitability
(GSS; Jackson 2001) and Growing Season Precipitation
(GSP; Blanco-Ward et al. 2007). The mathematical definitions of all these indices, as well as their main references, are
listed in Table 1, and will not be further detailed here.
Following two previous studies (Malheiro et al. 2010;
Santos et al. 2012b), an improved CompI is also computed
in the current study. The CompI at a given location is the
ratio of “optimal years” for winegrape growth over a given
time period to the total number of years in the period. An
“optimal year” must simultaneously fulfil the following
criteria: (1) HI ≥ 900 °C; (2) DI ≥ −100 mm; (3) HyI ≤
7,500 °Cmm; and (4) total absence of days with minimum
temperature below −17 °C. This new CompI differs from the
previous definitions in the first and third thresholds (cf.
Malheiro et al. 2010; Santos et al. 2012b). A lower HI
threshold (900 instead of 1,200 °C) is considered herein in
order to include viticultural regions in northern Europe with
marginally suitable winemaking conditions. In fact, in two
previous studies by Jones et al. (2010) and Hall and Jones
(2010), a Winkler Index (WI; Winkler 1974) above 850 °C
was found to be already suitable for winegrape growth in
western United States and Australia, respectively.
Additionally, (Santos et al. 2012b) showed a clear correspondence between the WI and HI patterns in Europe using
850 °C and 900 °C as lower limits, respectively. The threshold
in the third criterion of the CompI (HyI≤7,500 °Cmm) was
chosen so that only climatic conditions noticeably favourable
to pests/diseases in the vineyards are excluded. Furthermore,
when considering a lower threshold (e.g. 5,100 °Cmm), many
winemaking regions in central Europe become misleadingly
unsuitable (Santos et al. 2012b).
In order to better discriminate the optimal climatic
requirements of different winegrape varieties (Jones et al.
2005a), an innovative category analysis of the CompI is also
carried out. At each site, the CompI is first classified as a
function of three pre-defined HI classes , namely: 900≤HI<
1,500 °C; 1,500≤HI<2,100 °C; 2,100≤HI<3,000 °C. Then
Table 1 List of bioclimatic indices used for climatic viticultural zoning in Europe, along with their corresponding definitions and references. The
calculations (summations) are carried out using daily climate variables
Bioclimatic index
Definition
Units
Suitable threshold
Reference
Growing season suitability (GSS)
Ratio of days with T≥10 °C
°C
-
Jackson 2001
mm
-
Blanco-Ward et al. 2007
°C
> 900
Huglin 1978
°C
mm
<7,500
Branas et al. 1946
mm
>−100
Riou et al. 1994
Growing season precipitation (GSP)
Sept:
P
ðPÞ
April
P Precipitation (mm)
Huglin index (HI)
Sept:
P
April
ðT 10ÞþðTmax 10Þ
d
2
T Mean temperature (ºC)
Tmax Maximum temperature (ºC)
d Length of day coefficient
Hydrothermal index (HyI)
Aug:
P
ðT P Þ
April
T Mean temperature (ºC)
P Precipitation (mm)
Dryness index (DI)
Sept:
P
ðWo þ P Tv EsÞ
April
Wo Soil water reserve (mm)a
P Precipitation (mm)
Tv Potential transpiration in the vineyard (mm)
Es Direct evaporation from the soil (mm)
a
Wo should be equal to 200 mm at the beginning of the growing season. For the following months, Wo takes the value of the DI in the previous
month
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the leading HI category is identified an assigned to the site.
HI values below 900 °C are considered unsuitable for winegrape growth while values above 3,000 °C are relatively rare
in Europe (not shown). This analysis allows a varietydependent assessment of the climatic change impacts on
the viticultural sector.
Model data
Climate models remain a valuable tool in climate change
assessments (Solomon et al. 2011), despite their widely
reported limitations (e.g. Knutti et al. 2008; Hulme and
Mahony 2010), and are used here as a source of information
about future climate change, namely in the temperature and
precipitation fields in Europe.
Regarding the definitions of the selected bioclimatic indices (Table 1), daily mean, maximum and minimum 2-m air
temperatures and daily precipitation totals are used in their
calculations. These four daily atmospheric variables were
extracted from datasets of 16 state-of-the-art transient experiments generated by the EU-FP6 project ENSEMBLES (http://
ensembles-eu.metoffice.com; van der Linden and Mitchell
2009), in a total of 15 different Global Climate Model /
Regional Climate Model (GCM/RCM) chains (for the
ECHAM5/COSMO-CLM combination, two ensemble simulations are used). Table 2 lists the 16 model experiments, along
with their acronyms and main references. Only these 16
transient experiments provide records without data gaps in
the two selected future periods (2011–2040 and 2041–2070).
However, they provide a sufficiently representative sample of
models, with different parameterisations, spin ups, physics
and modelling approaches, covering a large amount of the
uncertainty inherent to numerical model simulations.
The A1B IPCC-SRES scenario (2001–2100), which corresponds to a moderate anthropogenic radiative forcing
(Nakićenović et al. 2000), was used. Other SRES emission
scenarios, such as A2, are not available for all ensemble
members and were therefore not considered for the present
analysis. However, the A1B and A2 scenarios start to clearly
diverge in their emission pathways only from 2070 onwards
(Nakićenović et al. 2000), later than the aforementioned two
future periods.
Daily model data was also extracted for the 40-year baseline period (C20; 1961–2000) so as to validate/calibrate it
using an observational dataset. The daily station-based gridded dataset (E-OBS, version-4) from the EU-FP6 project
ENSEMBLES (http://ensembles-eu.metoffice.com), provided
by the European Climate Assessment & Dataset (ECA&D)
project (http://eca.knmi.nl) in the baseline period, was used for
this aim. The original gridded data is defined over land areas at
201 grid boxes along latitude and 464 grid boxes along
longitude (0.25° latitude×0.25° longitude grid). This dataset,
despite some limitations, represents a valuable resource for
climate research in Europe (Hofstra et al. 2009). Detailed
information about the E-OBS dataset can be found in
Haylock et al. (2008). The baseline period (1961–2000) corresponds to the longest available common time-series
amongst the different datasets (observations and simulations).
All model datasets were bi-linearly interpolated (in latitude and longitude) from the original rotated grids (cf. their
original spatial resolution in Table 2) to a regular grid of
0.25° latitude×0.25° longitude to enable model validation/
calibration using exactly the same grid as in E-OBS. Only
the geographical sector [35-60°N; 12°W-36°E] where viticultural zoning is relevant is considered herein (102 grid
boxes along latitude×190 grid boxes along longitude). The
selected bioclimatic indices were then computed for all grid
points with available data (excluding blank/missing data) in
the E-OBS (11,506 grid cells). Statistically significant differences at a 99 % confidence level (using the two-sample
Student’s t-test) between the future period of 2041–2070
and the baseline period for HI, DI, HyI and CompI were
also computed and plotted.
Model output statistics and model uncertainties
Several studies have demonstrated that equal weights are
generally a better approach than performance-based weights
for computing ensemble mean statistics (Christensen et al.
2010; Weigel et al. 2010). For this reason, only equally
weighted ensemble mean patterns for 2041–2070 (higher
greenhouse gas forcing) are presented henceforth.
However, an inter-comparison of model performances in
reproducing the observed mean patterns of the selected
bioclimatic indices in the baseline period (1961–2000) was
undertaken in order to summarise and highlight the most
important deviations (bias) between simulated and E-OBS
mean patterns. Scatterplots representing the different models
as a function of their spatial mean bias (MB) and absolute
spatial mean bias (AMB; less sensitive to extremes then the
root mean square deviation) in the HI, DI and HyI patterns
are shown as supplementary material (Fig. S1).
Due to the model bias referred above, MOS are used to fit
raw model data to observations, as the statistical distributions of the simulated data present biases with respect to
observations (Wilks 2006). This is also a common procedure when assessing climate projections (Mearns et al.
2001). Linear transfer-functions are applied to obtain transformed (adjusted) data with the same mean patterns as the
observational data. Taking into account the large number of
grid boxes (11,506), higher order polynomial, exponential
or logarithmic transformations were not tested. This is not a
clear shortcoming, mainly because the bioclimatic indices
are defined on a yearly basis and most of them are normally
distributed, according to the Lilliefors test applied to all
indices and simulations (not shown); only a few exceptions
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Table 2 Summary table of all Global Climate Model / Regional
Climate Model (GCM / RCM) model chains used in this study. The
corresponding acronyms, institutions and original grid resolutions are
also listed. In all simulations the period 2011–2070 was used under the
IPCC-SRES A1B scenario. References relevant to each chain are also
indicated
Acronym
GCM
RCM
Original grid
Institution
References
ARP Aladin(CNRM)
ARP HIRHAM(DMI)
BCM RCA(SMHI)
ARPEGE-RM5.1
ARPEGE
BCM
CNRM-Aladin
DMI-HIRHAM
SMHI-RCA
25 km
25 km
25 km
CNRM
DMI
SMHI
EH5 RACMO(KNMI)
EH5 CLM1(MPI)
ECHAM5-r3
ECHAM5-r1
KNMI-RACMO2
COSMO-CLM-1
25 km
18 km
KNMI
MPI-M
EH5 CLM2(MPI)
ECHAM5-r2
COSMO-CLM-2
18 km
MPI-M
EH5 HIRHAM(DMI)
EH5 RCA(SMHI)
ECHAM5-r3
ECHAM5-r3
DMI-HIRHAM
SMHI-RCA
25 km
25 km
DMI
SMHI
EH5 RegCM(ICTP)
ECHAM5-r3
ICTP-RegCM3
25 km
ICTP
Gibelin and Deque 2003
Christensen et al. 1996
Kjellström et al. 2005
Samuelsson et al. 2011
Lenderink et al. 2003
Böhm et al. 2006
Steppeler et al. 2003
Böhm et al. 2006;
Steppeler et al. 2003
Christensen et al. 1996
Kjellström et al. 2005
Samuelsson et al. 2011
Elguindi et al. 2007
Pal et al. 2007
EH5 REMO(MPI)
ECHAM5-r3
MPI-REMO
25 km
MPI-M
HC CLM(ETHZ)
HadCM3Q0 (normal sens)
ETHZ-CLM
25 km
ETHZ
HC
HC
HC
HC
HadCM3Q0 (normal sens)
HadCM3Q16 (high sens)
HadCM3Q3 (low sens)
HadCM3Q3 (low sens)
HC-HadRM3Q0
HC-HadRM3Q16
HC-HadRM3Q3
SMHI-RCA
25
25
25
25
HC
HC
HC
SMHI
HadCM3Q16 (high sens)
C4I-RCA3
25 km
HadRM3Q0(HC)
HadRM3Q16(HC)
HadRM3Q3(HC)
RCA(SMHI)
HC RCA3(C4I)
were identified over some mountainous areas (e.g. the
Alps).
The linear transformations were then applied to the yearly bioclimatic indices, at each grid box, and for each model
dataset individually. The transfer-functions between indices
calculated from E-OBS and indices calculated from the
models were estimated for the 40-year baseline period
(1961–2000). This scaling procedure has been used in previous studies (Alexandrov and Hoogenboom 2000; Santos
et al. 2012a; Fraga et al. 2012; Jakob Themeßl et al. 2011)
and enables the correction of biases in the model simulations, also enabling a comparison between the different
RCMs. As such, for 1961–2000, the corrected data of each
index are equal amongst the different simulations and equal
to the corresponding E-OBS data. Therefore, for the baseline period, only results obtained using E-OBS are presented. The same transfer-functions (coefficients) were
applied to all datasets in the two future periods (2011–
2040 and 2041–2070), leading to corrected patterns for
future conditions. In this procedure, the temporal invariance
of the transfer-function between recent-past and future is a
km
km
km
km
C4I
Jacob and Podzun 1997
Jacob 2001
Steppeler et al. 2003
Jaeger et al. 2008
Collins et al. 2011
Collins et al. 2011
Collins et al. 2011
Kjellström et al. 2005
Samuelsson et al. 2011
Kjellström et al. 2005
Samuelsson et al. 2011
basic underlying assumption. In any case, it must be kept in
mind that the patterns of the mean differences between a
future period and the baseline period (climate change signal)
are independent of these corrections. Also, applying
transfer-functions to the indices and not to the original
climatological data is a new methodology that may require
further research.
Measuring model uncertainty is also crucial when assessing future impacts of climate change. For this purpose, the
16-member normalised interquartile range (third quartile
minus first quartile divided by the mean at each grid point;
NIQR hereafter) of the HI, DI, HyI and CompI is also
assessed and plotted. The spatial correlations of the CompI
between the 16 simulations and the ensemble mean are also
presented so as to clarify the spatial coherence amongst
them.
Inter-annual variability
As previously stated, irregularity in the yields and wine
quality is an additional pitfall for the winemaking sector.
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Significant alterations in the inter-annual variability could
lead to large economic impacts for this perennial crop. The
ratios between the averages of the 16 inter-annual standarddeviations (calculated for each of the 16 ensemble members
separately) in 2041–2070 and 1961–2000 of the bioclimatic
indices (HI, DI and HyI) are computed to assess changes in
inter-annual variability.
Results
Recent-past viticultural zoning
The HI has been shown to be an effective tool for viticultural
zoning and has been thus widely applied (Jones et al. 2010). Its
ensemble mean pattern (Fig. 1a) is highly coherent with the
GSS pattern (not shown) and highlights the fact that large areas
of southern and central Europe are suitable for winegrape
growth, whereas regions northwards of the 53°N parallel are
generally unsuitable. In this context, it should also be emphasised that different classes of the HI are related to grapevine
varieties with different thermal requirements (Huglin 1978).
Hence, lower values do not necessarily mean lower suitability,
but rather conditions that might be optimal for specific varieties
(e.g. white grapevine varieties are generally favoured by cooler
climates; Duchene and Schneider 2005).
The mean patterns for DI and HyI (Fig. 1b,c) give opposite perspectives of the humidity requirements for optimal
grapevine development. Severe dryness (assessed by the DI)
and excessive humidity (assessed by the HyI) during the
growing season commonly have detrimental impacts on the
different stages of the grapevine development (Branas et al.
1946). Overall, the DI pattern depicts a remarkable contrast
between southern Europe and central and northern Europe,
and shows that dryness might be a limitation only over small
areas of southern Europe and in certain years (values
below −100 mm), whilst excessive humidity levels (HyI
above 5,100 °Cmm) are generally restricted to Atlantic
coastal areas or to mountainous areas, such as the Alps or
the Carpathian range. The baseline period mean pattern for
the CompI (Fig. 1d) demonstrates this index’s usefulness in
viticultural zoning, since it is coherent with the spatial
distribution of well-known traditional viticultural regions,
showing higher suitability in the southern European areas.
Composite index vs viticultural regions
As mentioned in section “Bioclimatic indices”, the CompI is
an attempt to characterise the most relevant atmospheric
requirements for winegrape growth with a single index.
According to our analysis, areas characterised by values of
the CompI in excess of 0.5 (50 % of optimal years) in the
period 1980–2009 encompass the most worldwide famous
winemaking regions in Europe (circles in Fig. 1e), which are
located mostly over southern and central Europe, particularly in countries such as France, Italy, Spain, Portugal and
Germany). This attests to the utility of this index for
European viticultural zoning. The more recent period
1980–2009 was chosen because it more closely reveals the
present time atmospheric conditions reflected in current
winemaking regions. As E-OBS data, used for model calibration, is available for 697 out of a total of 754 winemaking
regions, only the former regions are taken into account
(most of the missing regions are islands or coastal areas).
From these 697 regions, about 93 % present a CompI equal
to or above 0.5, i.e., are in agreement with the CompI
pattern.
Future viticultural zoning
The HI patterns for the future period (Fig. 2a) shows a
northward displacement and a stronger increase over southern and Western Europe. This reveals an apparent northward
extension of the high suitability areas for 2041–2070. In
fact, new suitable regions for grapevine development within
the latitude belt 50-55°N are projected to arise, which is in
line with the results obtained for GSS (not shown).
Furthermore, important changes over southern Europe
should also be expected under anthropogenic radiative
forcing.
For the DI in the future period (Fig. 2b) there is a
significant drying over most of the southern half of
Europe, which is in agreement with the projected changes
in the GSP (not shown). These changes are likely to yield
severe dryness (DI<−100 mm) in areas such as southern
Iberia, Greece and Turkey. On the other hand, the projected
changes in the HyI (Fig. 2c) reveal an enhancement of the
humidity levels over central and eastern Europe, explained
largely by the joint effect of warmer and moister conditions
in the future (increase in the GSP and in the growing season
temperature). Therefore, while dryness may represent a
threat/challenge for winegrape growth in southern Europe
(e.g. harmful water-stress), excessive humidity in central
and eastern Europe can potentially trigger pests and diseases
in the vineyards (e.g. outbreaks of downy mildew disease).
The ensemble mean pattern for the CompI (Fig. 2d)
reveals a general decrease in suitability in southern
European regions, especially due to the increased dryness
in these areas. Conversely, large areas of central and
Western Europe are projected to become more suitable for
viticulture, due to the more favourable thermal conditions.
Climate change signals and uncertainties
When assessing climate change projections, it is important
to analyse not only the climate change signal itself, but also
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Fig. 1 a Huglin Index (HI), b Dryness Index (DI), c Hydrothermal
Index (HyI) and d Composite Index (CompI) for the baseline period
(1961–2000). e European wine regions (circles) with the
corresponding CompI value (cf. scale) for the period of 1980–2009
using the E-OBS data. Source of the wine regions locations: Wine
Regions of the World—Version 1.31 (URL: http://geocommons.com/
overlays/3547)
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Fig. 2 As Fig. 1 but for the future period of 2041–2070 under the A1B IPCC-SRES scenario
the respective model uncertainties (Deser et al. 2012). The
spatial correlations for the CompI pattern among the 16members and their mean (Table S1) shows that most models
are clearly inter-correlated, as well as with their ensemble
mean (correlation coefficients above 0.7). The models
showing higher correlations with the ensemble mean are
the ARP HIRHAM (DMI), EH5 REMO (MPI), HC CLM
(ETHZ) and HC HadRM3Q0 (HC). On the other hand, the
models showing lower correlations are BCM RCA (SMHI)
and EH5 RegCM (ICTP).
CompI maps for the models with the lowest (HC RCA3
(C4I); Fig. 3a) and highest (HC RCA (SMHI); Fig. 3b)
spatial means of this index are also presented. Although
their spatial patterns may show important differences, they
are considered as equally probable in the present study
(equal weights in the statistical measures).
To assess the ensemble variability in the future projections (uncertainty) for the CompI in 2041–2070, some statistical measures [means, medians, minima, maxima, NIQR
and total ranges (TR)] are provided in Table 3 for a selection
of well-known grapevine growing regions throughout
Europe. The means reveal CompI values always higher than
0.80 but for two regions (Alentejo-Borba, Tokaj-Hegyalja;
cf. Fig. 2d), the medians are consistently higher than the
means, which reflects the negative skewness of the distributions (CompI upper limit of 1.0). The minima show a
large variability, with values ranging from 0.00 (TokajHegyalja) up to 0.99 (Champagne), while the maxima are
always 1.00, with only two exceptions (Alentejo-Borba,
Tokaj-Hegyalja). Regarding the ranges of variability, since
TR is more affected by extremes than NIQR, some discrepancies between them are apparent; a higher TR does not
necessarily imply a higher NIQR or vice-versa. Considering
TR as an uncertainty measure, some regions reveal more
pronounced uncertainties (Rheinhessen, Ribera del Duero,
Chianti, Porto/Douro, Barolo, La Mancha, Alentejo-Borba,
Tokaj-Hegyalja), whereas others present relatively low
uncertainties and CompI minima above 0.70 (Champagne,
Coteaux du Loire, Bordeaux, Mosel, Rioja, Rheingau,
Vinhos Verdes, Alsace).
The HI, DI, HyI and CompI climate change signals
(difference between the ensemble means for 2041–2070
and 1961–2000) are now presented, along with the
corresponding NIQR (Fig. 4). The latter is adopted as a
measure of the model uncertainties. The significant
increases in the HI values over most of Europe (left panel
Fig. 4a) is associated with a relatively low uncertainty (right
panel, Fig. 4a), with the exception of northern Great Britain
and the Alps. Conversely, the changes in the DI pattern (left
panel Fig. 4b) show large uncertainties, particularly in the
regions where changes are most pronounced (the
Mediterranean basin; right panel Fig. 4b). The HyI shows
Int J Biometeorol
Fig. 3 a Composite index (CompI) of the more severe model [HC RCA3 (C4I)]. b CompI showing the less severe model [HC RCA (SMHI)], for
the time period 2041–2070
significant increases in eastern, northern and central Europe,
while important decreases are found over southern and western
Europe (left panel Fig. 4c). For this pattern, low uncertainty
levels are displayed all across Europe (right panel Fig. 4c).
Contrary to the DI, this index combines temperature with
precipitation and its lower uncertainty is thereby also influenced by temperature. The CompI undergoes significant
increases over large areas of northern, eastern and central
Europe, whereas decreases can be found in some areas of
southern Europe, mainly southern Iberia, southern Italy and
Greece (left panel Fig. 4d). This index reveals high uncertainties in many eastern European regions (right panel Fig. 4d),
where the strongest climate change signal is found, changing
from recent-past climate conditions mostly unsuitable for viticulture to much higher suitability in the future. In these circumstances, slight differences in the climate conditions and/or
projections tend to become very significant, in relative terms.
Table 3 Ensemble means, medians, minima, maxima, normalised
interquartile ranges (NIQR) and total ranges (TR) of the Composite
Index (CompI) in 2041–2070 and for a selection of European famous
winemaking regions (the respective grid box coordinates are also
listed). Regions are ranked in ascending order with respect to their
TR values
Country
Region
Longitude/ latitude (º)
France
France
France
Germany
Spain
Germany
Portugal
France
Germany
Spain
Champagne
Coteaux du Loire
Bordeaux
Mosel
Rioja
Rheingau
Vinhos Verdes
Alsace
Rheinhessen
Ribera del Duero
4.003/
−0.846/
−0.055/
6.734/
2.402/
7.944/
−8.545/
7.6592/
8.254/
−4.465/
Italy
Portugal
Italy
Spain
Portugal
Hungary
Chianti
Porto/Douro
Barolo
La Mancha
Alentejo-Borba
Tokaj-Hegyalja
11.040/
−7.555/
8.545/
2.698/
−7.424/
21.349/
Categorisation of CompI
The CompI was analysed with respect to three relevant
classes of the HI over Europe, namely: 900≤HI<1,500 °C;
1,500≤HI<2,100 °C; 2,100≤HI<3,000 °C, as explained in
“Materials and methods”. Hence, the CompI was split into
Mean
Median
Minimum
Maximum
NIQR
TR
49.155
47.368
45.012
49.765
42.493
49.988
41.570
48.660
49.936
41.609
0.98
0.99
0.96
0.95
0.96
0.96
0.94
0.93
0.94
0.94
1.00
1.00
0.97
0.97
1.00
1.00
1.00
0.97
0.97
1.00
0.93
0.90
0.90
0.87
0.78
0.77
0.73
0.70
0.63
0.55
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.03
0.02
0.10
0.08
0.05
0.03
0.07
0.09
0.05
0.05
0.07
0.10
0.10
0.13
0.22
0.23
0.27
0.30
0.37
0.45
43.640
41.170
41.570
39.653
38.797
48.187
0.90
0.92
0.86
0.82
0.55
0.71
0.97
1.00
0.93
0.90
0.57
0.87
0.52
0.47
0.47
0.30
0.17
0.00
1.00
1.00
1.00
1.00
0.93
0.97
0.12
0.05
0.15
0.2
0.41
0.13
0.48
0.53
0.53
0.70
0.76
0.97
Int J Biometeorol
Fig. 4 Left panels Differences in the mean patterns (2041–2070
minus 1961–2000), right panels NIQR (third quartile minus first
quartile divided by the mean at each grid point) showing variability
in the 16-member ensemble. Differences not statistically significant
(NS) at the 99 % confidence level are shaded grey. a HI, b DI, c
HyI, d CompI
Int J Biometeorol
three non-overlapping meaningful categories for viticultural
zoning in Europe. Each year at a given location is thus
keyed to one of these categories. The leading category at
each location and for each selected time period is then
detected and plotted. Only grid cells with at least 50 % of
optimal years according to the total CompI (values equal to
or higher then 0.5) were categorized.
For the baseline period (Fig. 5a), this pattern clearly
highlights that approximately all potential winemaking
regions north of the 45°N parallel are keyed to the first
category (associated with the HI first class, Fig. 5b). At
lower latitudes, the most important exception is verified
over the northern Iberian Peninsula, where large areas are
also keyed to the first category; other minor exceptions
occur over high altitude regions in southern Europe. The
second and third categories are widespread over southern
Europe (nearer the Equator than 45°N), with low altitude
(warmer) areas in the Mediterranean Basin depicting a preponderance of the third category, such as in southwestern
Iberia, northern Italy (Po valley) and many coastal areas.
For both future periods (Fig. 5b, c) there is strong evidence
for new regions suitable for viticulture in different parts of
northern Europe (e.g. southern British Isles, the Netherlands,
Denmark, northern Germany and Poland). In fact, the projected changes in the number of suitable grid boxes (CompI
greater than 0.5) for 2041–2070 shows and increase poleward
of the 50°N parallel (Fig. 6), though it gradually weakens
towards 60°N (upper latitude limit in the present study).
Conversely, many regions in southern Europe (nearer the
Equator than 41°N), such as southern Iberia, shift to unsuitable conditions, which can be explained largely by the lack of
precipitation and dryness (Fig. 2b). Within the latitude belt of
41–50°N there is no remarkable change in the number of
suitable grid boxes, though there are some very significant
shifts amongst the three categories.
On the whole, new suitable regions northwards of 50°N are
keyed mainly, as expected, to the first category (Figs. 5, 6).
Further, most regions from 46°N up to 50°N tend to change
from the first to the second category, while many regions
within 39–46°N are projected to change from the first or
second category to the third category. Therefore, the first
category (the least heat demanding) is projected to be located
essentially northwards of 50°N, as no new regions of this
category are expected to arise at lower latitudes. The second
and third categories undergo a northward displacement, leaving many current winemaking regions in southern Europe
with unsuitable conditions, primarily because of the severe
dryness that is expected to prevail in their future climates.
Inter-annual variability
Due to the relevance of the inter-annual variability in viticultural zoning, corresponding climate change projections
are also analysed. As stated above, apart from a few exceptions, the selected bioclimatic indices are normally distributed and their inter-annual variability can thus be assessed
by their sample standard-deviation. The map of the ratios
between the ensemble mean inter-annual standarddeviations of the HI for 2041–2070 and 1961–2000 reveals
values significantly above one (higher variability) over large
areas of Europe, particularly over central Europe, the British
Isles and over some mountainous regions, such as the Alps
(Fig. 7a). These results also depict low model uncertainties,
with the exceptions of some regions of Iberia and some
regions of central and northeastern Europe (Fig. 7b).
The DI inter-annual variability (Fig. 7c) also shows an upward trend, especially in central and eastern European regions,
with larger values over Great Britain. The projected increases in
precipitation over these regions, combined with increased interannual variability, may become harmful to viticulture, with
enhanced risks of pests and diseases. These results also reveal
low model uncertainty (Fig. 7d). In addition to the increase in
the inter-annual variability of the HI and DI, the HyI shows
significant increases in its inter-annual variability over northeastern Europe (not shown). As such, there is evidence for an overall
increase in the inter-annual variability under future climates,
which may also comprise an increase in the occurrence of
extremes, although this analysis is left to a forthcoming study.
Summary and discussion
The projected and well-documented warming over Europe (cf.
Christensen et al. 2007) leads to changes in the thermal indices
(GSS and HI; Fig. 2a), with significant increases, particularly
over southern and western Europe (over 400 °C increase in HI
for 2041–2070). Further, dryness conditions in the growing
season (GSP and DI; Fig. 2b) are projected to undergo an
enhancement over southern Europe, while humidity levels
(GSP and HyI; Fig. 2c) are expected to remain relatively high
over central and northern Europe. All these changes are also
corroborated by the CompI, not only by its aggregate values
(Fig. 2d), but also in relation to the different classes of the HI
considered for category analysis (Figs. 5, 6).
These projections are consistent with other recent studies
by Neumann and Matzarakis (2011), for Germany, and by
Duchene and Schneider (2005), for Alsace, France.
Analogous changes in the mean patterns of HI (Fig. 2a) have
already been reported, including an increase of about 300
units for six winegrowing European regions over the last
30–50 years (Jones et al. 2005a). Additionally, under a future
warmer climate, higher temperatures (above 30 °C) may often
inhibit the formation of anthocyanin (Buttrose et al. 1971) and
thus reduce grape colour (Downey et al. 2006). All these
changes may imply the selection of new winegrape varieties
and a reshaping of the European viticulture.
Int J Biometeorol
Fig. 5 Leading categories in
the CompI (HI classes 1, 2 and
3) for the baseline period (a)
and the two future periods (b, c)
Analysis of the GSP and DI provides evidence of very
dry climates over southern Europe (Fig. 2b). Although grape
quality is generally favoured by moderate water stress conditions during berry ripening (Koundouras et al. 1999;
Int J Biometeorol
Fig. 6 Latitudinal differences (2041–2070 minus 1961–2000) in the
number of grid cells equal to or above 0.50 in the CompI
Santos et al. 2003), severe dryness (DI below −100 mm) in
many Mediterranean-like climate regions may be damaging
(Chaves et al. 2010); some of these regions are actually
projected to have a GSP below 200 mm (southern Iberia,
southern Italy, Turkey and Greece). Changes in winemaking
practices are thereby expected to arise in these regions,
including crop irrigation or water stress mitigation methods
(Flexas et al. 2010). In the wetter regions in central and
Fig. 7 a, c Ratio between the averages of the 16 inter-annual standarddeviations (calculated for each of the 16 ensemble members separately)
in 2041–2070 and 1961–2000 of the HI (a), and DI (c). b, d NIQR
northern Europe, however, pests and diseases can also be a
drawback to wine production, since changing climate conditions may modify the complex interrelationships between
vine, pest and disease development (Stock et al. 2005).
Regarding downy mildew, patterns of change in the HyI
suggest, at first sight, low risks of contamination in southern
Europe, but moderate-to-high risk at higher latitudes, the
latter as a result of warm and wet conditions. Nonetheless,
implications of a future increase in the HyI at the continental
scale may not be too dramatic, mainly because, in the
emerging winemaking areas, where thermal conditions will
gradually become favourable to wine production, the HyI
will remain below the maximum threshold of 7,500 °Cmm
in most years.
Further, a projected strengthening of the inter-annual
variability in the HI and DI may result in additional constraints for this perennial crop and for the winemaking
sector as a whole, particularly in view of an increasing
irregularity and unpredictability of yields and wine quality.
A reshaping of the European regions suitable for grapevine growing is likely to occur, taking into account the
projections of CompI. Many regions throughout Europe
are shown to be undergoing a change to a higher CompI
category. As an illustration, some regions in France and
showing variability in the 16-member ensemble for the ratios of the HI
(b) and DI (d). Non statistically significant ratios (NS) are shaded grey
Int J Biometeorol
Germany will in the future present similar values to those
seen today in the Mediterranean Basin. A general decrease
in the number of suitable regions below 39°N might also be
expected. This outcome is also supported by previous findings (e.g. Kenny and Harrison 1992; Jones et al. 2005b;
Stock et al. 2005; Fraga et al. 2012). Overall, shifts in
CompI classifications throughout Europe suggest modifications in the suitability of a given region to a specific winegrape variety and are expected to influence wine yield/
quality attributes.
The assessment of the uncertainty associated with the
climate change projections is of great importance for
informing decision-makers. Following the recommendations in Christensen et al. (2010) and Weigel et al. (2010)
in our study, model uncertainty for viticultural zoning was
assessed by considering all members of the ensembles with
equal weight.
Although the ensemble means shown in the current study
are in overall agreement with a previous study that employed a
single model for similar purposes (Malheiro et al. 2010), thus
generally corroborating previous findings, future climate conditions can depend greatly on the model experiment at local/
regional scales (Dessai and Hulme 2007). In fact, at these
scales, remarkable differences were found between singlemodel projections and the ensemble projections presented
here, which plainly substantiate the current study.
The increase in CO2 concentration is expected to be beneficial for winegrape growth (Moutinho-Pereira et al. 2004;
Goncalves et al. 2009; Bindi et al. 1996). Reflecting the
settings of the ENSEMBLES project (van der Linden and
Mitchell 2009) in this study we considered only the A1B
emission pathway. A more pronounced increase in atmospheric CO2 concentration is predicted by the A2 emission scenarios, especially relevant to the second half of the century, with
implications that need to be examined in future studies.
The impacts of the aforementioned changes in climate
suitability for the winemaking sector in Europe, can be
summarized as follows: (1) some southern regions (e.g.
Portugal, Spain and Italy) may face detrimental impacts
owing to both severe dryness and unsuitably high temperatures; (2) regions in western and central Europe (e.g.
southern Britain, northern France and Germany), some of
which are world-renowned winemaking regions, might benefit from future climate conditions (higher suitability for
grapevine growth and higher wine quality); and (3) new
potential winegrape growth areas are expected to arise over
northern and central Europe, where conditions are currently
either marginally suitable or too cold for this crop. It is still
worth mentioning that the higher inter-annual variability
(climate irregularity) in the future over most of Europe
may lead to additional threats to this sector.
Furthermore, the shortening of the growing season resulting in earlier phenological events, reported by several
studies (Webb et al. 2012; Bock et al. 2011; Daux et al.
2011; Chuine et al. 2004), may indicate the need to adapt the
time periods and classes in which the bioclimatic indices are
commonly calculated. Since grapevine phenology and wine
quality were shown to be correlated with HI (Jones et al.
2005a; Orlandini et al. 2005), this limitation can, to some
extent, be overcome by adding new classes to the existing
classical bioclimatic indices, instead of modifying the time
periods (difficult to achieve due to the extent of the regions
studied). A similar approach was indeed carried out by
Santos et al. (2012b) using a new low-limit class for the
HI (900–1,200 °C).
Future changes in the viticultural zoning in Europe impose new challenges for the winemaking sector. These modifications give clues to the development of appropriate
strategies to be taken by the winemaking sector to face
climate change impacts. There have already been reports
of acclaimed winemaking regions that may become unsuitable for premium wine production within the current century
(Hall and Jones 2009). As the patterns discussed in this
study are available at a relatively high spatial resolution
(25–30 km), regional climate change assessments are permitted, enabling the development of local adaptation and
mitigation measures, drawn specifically for the winemaking
sector. Changes in soil and crop managements, as well as
genetic breeding and oenological practices, are crucial for a
better adjustment between viticulture and future environment. These measures need to be planned adequately and
timely by stakeholders, policymakers, and by the different
socioeconomic sectors that are directly or indirectly influenced by the vineyard and winemaking activities.
Acknowledgements We acknowledge the ENSEMBLES project
(contract GOCE-CT-2003-505539), supported by the European
Commission’s 6th Framework Programme (EU FP6) for supplying
the model datasets (http://ensembles-eu.metoffice.com/). We thank
Dr. Joaquim Pinto, at the University of Cologne, the German Federal
Environment Agency and the COSMO-CLM consortium for providing
COSMO-CLM data. We also acknowledge E-OBS and the data providers in the ECA&D project (http://eca.knmi.nl). This study was
carried out under the Project Short-term climate change mitigation
strategies for Mediterranean vineyards (Fundação para a Ciência e
Tecnologia - FCT, contract PTDC/AGR-ALI/110877/2009). This work
is also supported by European Union Funds (FEDER/COMPETE Operational Competitiveness Programme) - under the project FCOMP01-0124-FEDER-022692. H.F. also thanks the FCT for providing a
research scholarship (BI/PTDC/AGR-ALI/110877/2009).
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