ORIGINAL PAPER

Future scenarios for viticultural zoning in Europe: ensembleprojections 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 grapevinegrowth are limited geographically and may be further chal-lenged by a changing climate. Due to the importance of thewinemaking sector in Europe, the assessment of futurescenarios for European viticulture is of foremost relevance.A 16-member ensemble of model transient experiments(generated by the ENSEMBLES project) under a green-house gas emission scenario and for two future periods(2011–2040 and 2041–2070) is used in assessing climatechange projections for six viticultural zoning indices. Aftermodel data calibration/validation using an observationalgridded daily dataset, changes in their ensemble meansand inter-annual variability are discussed, also taking intoaccount the model uncertainties. Over southern Europe, theprojected warming combined with severe dryness in thegrowing season is expected to have detrimental impacts onthe grapevine development and wine quality, requiringmeasures to cope with heat and water stress. Furthermore,the expected warming and the maintenance of moderatelywet growing seasons over most of the central Europeanwinemaking regions may require a selection of new grape-vine varieties, as well as an enhancement of pest/diseasecontrol. New winemaking regions may arise over northernEurope and high altitude areas, when considering climaticfactors only. An enhanced inter-annual variability is alsoprojected over most of Europe. All these future changespose new challenges for the European winemaking sector.

Keywords European viticultural zoning . Bioclimaticindices .Climate change . Ensemble projections .Viticulture .

Model uncertainties

AbbreviationsCompI Composite indexDI Dryness indexECA&D European climate assessment and datasetGCM Global climate modelGSP Growing season precipitationGSS Growing season suitabilityHI Huglin indexHyI Hydrothermal indexIPCC International panel on climate changeMOS Model output statisticsNIQR Normalized interquartile rangeRCM Regional climate modelTR Total rangeSRES 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 physiol-ogy and its development phases are determined mostly byspecific environmental conditions (e.g. Magalhães 2008). Infact, monthly mean temperatures and precipitation totals in thegrowing season present significant correlations with grape-vine yield in many regions (e.g. Makra et al. 2009; Santos etal. 2012a).

Grapevine is a heat demanding crop, requiring a 10 °Cbasal temperature for its growing cycle onset and develop-ment (Amerine and Winkler 1944; Winkler 1974), and

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. SantosCentre for the Research and Technology of Agro-Environmentaland Biological Sciences (CITAB), University of Trás-os-Montes eAlto Douro (UTAD), PO Box 1013, 5001-801 Vila Real, Portugale-mail: hfraga@utad.pt

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relatively high solar radiation intensities (e.g. Magalhães2008). However, prolonged exposure to excessive heat (e.g.temperatures above 40 °C) may have detrimental impacts onsome physiological processes (Berry and Bjorkman 1980;Osorio et al. 1995), resulting in poor yields and quality(Kliewer 1977; Mullins et al. 1992). Although grapevinesare also resistant to relatively low temperatures (lower thermallethal limit of approximately −17 °C) during early stages(Hidalgo 2002), frost occurrences during spring can severelydamage crop production (e.g. Spellman 1999). Further, exces-sive humidity in spring can trigger pests and diseases, such asdowny mildew (Carbonneau 2003), while severe drynessduring the growing season can also lead to harmful waterstress (Koundouras et al. 1999) thus leading to reductions ingrapevine productivity (Moutinho-Pereira et al. 2004).

Given these considerations, climate change brings new andmajor challenges for winegrape growers. In fact, projectedfuture changes over Europe under the A1B InternationalPanel on Climate Change (IPCC) – Synthesis Report onEmission Scenarios (SRES) scenario (Nakićenović et al.2000) include a temperature increase of 2.3–5.3 °C in northernEurope and 2.2–5.1 °C in southern Europe by the end of thetwenty-first century (Christensen et al. 2007). In addition, forthe same scenario, it has been shown that temperatureextremes are also expected to increase throughout Europe(Andrade et al. 2012).

Changes in inter-annual variability and extremes in cli-matic factors result in shifts in grapevine phenology, diseaseand pest patterns, lower predictability and regularity of theyields 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 con-centrations under future atmospheric conditions may alsoplay a key role in grapevine physiology and yield attributes(Moutinho-Pereira et al. 2009), though this forcing is out ofthe scope of the present study.

Taking this climatic forcing into account, current grape-vine global geographical distribution is limited largely toregions where the growing season mean temperature (April–September in the Northern Hemisphere) is within the 12–22 °C range (Jones 2006). Taking the aforementioned deli-cate balance between environmental conditions and viticul-tural zoning into account, climate change is likely to haveconsiderable regional impacts not only on grapevine attrib-utes, but also on wine quality (e.g. Jones and Davis 2000;Jones et al. 2005b). Due to the high socioeconomic rele-vance of the winemaking sector in many European regions,the assessment of the impacts of climate change onviticulture is of the utmost importance, particularly forthe most renowned winemaking regions spread over thecontinent. Winegrape growers are indeed becoming in-creasingly aware of these changes and their resultingimpacts (Battaglini et al. 2009).

Future climatic zones are then expected to shift pole-wards, leading to changes in the current regions suitablefor grapevine growing and to new potential winemakingregions. In addition, due to the projected decrease in theannual precipitations over southern Europe (Christensen etal. 2007), grapevines are also expected to be affected neg-atively by severe dryness (Koundouras et al. 1999; Santos etal. 2003).

Several studies in different areas of research used ensem-bles of model runs in assessing climate change projectionsunder anthropogenic radiative forcing. As simple illustra-tions, Lobell et al. (2006) used a set of regional climatemodels to assess the impacts of climate change on perennialcrop 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 ensembleprojections enables quantification of numerical modeluncertainties arising from differences in physics and mod-elling approaches, model parameterizations and initialisa-tions, among others. This is important because modeluncertainty may lead to different outcomes (Deser et al.2012). Another advantage of this approach is that multi-model ensembles commonly outperform studies based onprojections from a single model (Knutti et al. 2010). Besidesusing statistical methodologies for model validation/calibra-tion, enabling correction of model bias, quantification of theuncertainty associated to climate change projections may beas important to the winemaking sector as the climate changesignal itself.

The present study assesses potential changes in the suit-ability of a given region for winegrape growth in Europeunder human-driven climate change. For this purpose, sev-eral bioclimatic indices, specifically developed for viticul-tural zoning, are applied using datasets from a 16-membermulti-model ensemble under the A1B emission scenario for2011–2040 and 2041–2070 (future periods). Hence, thisstudy is structured as follows: (1) a multi-model ensembleis used for assessing climate change projections in a set ofviticultural bioclimatic indices, (2) a validation with a state-of-the-art observational gridded daily dataset is conductedand calibration techniques are then applied to the modeloutputs, (3) the ensemble projections and the correspondingmodel uncertainties are analysed, (4) an analysis based on acategorized Composite Index (CompI) is carried out, and (5)the projected changes in the inter-annual variability areassessed. In the next section, the bioclimatic indices aredefined, model data is presented, model output statistics(MOS) and model uncertainty are discussed. In the“Results” section, after an initial model skill inter-comparison, the bioclimatic indices are presented, alongwith their inter-annual variability. An analysis of each indi-vidual 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 ofthis study will be summarized and discussed.

Materials and methods

Bioclimatic indices

The implications of climate change on viticultural zoning inEurope were assessed on the basis of projections for thefollowing bioclimatic indices: Huglin Index (HI; Huglin1978), Dryness Index (DI; Riou et al. 1994), HydrothermalIndex (HyI; Branas et al. 1946), Growing Season Suitability(GSS; Jackson 2001) and Growing Season Precipitation(GSP; Blanco-Ward et al. 2007). The mathematical defini-tions of all these indices, as well as their main references, arelisted 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 computedin the current study. The CompI at a given location is theratio of “optimal years” for winegrape growth over a giventime period to the total number of years in the period. An“optimal year” must simultaneously fulfil the followingcriteria: (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 theprevious definitions in the first and third thresholds (cf.Malheiro et al. 2010; Santos et al. 2012b). A lower HIthreshold (900 instead of 1,200 °C) is considered herein inorder to include viticultural regions in northern Europe withmarginally suitable winemaking conditions. In fact, in twoprevious studies by Jones et al. (2010) and Hall and Jones(2010), a Winkler Index (WI; Winkler 1974) above 850 °Cwas found to be already suitable for winegrape growth inwestern United States and Australia, respectively.Additionally, (Santos et al. 2012b) showed a clear corre-spondence between the WI and HI patterns in Europe using850 °C and 900 °C as lower limits, respectively. The thresholdin the third criterion of the CompI (HyI≤7,500 °Cmm) waschosen so that only climatic conditions noticeably favourableto pests/diseases in the vineyards are excluded. Furthermore,when considering a lower threshold (e.g. 5,100 °Cmm), manywinemaking regions in central Europe become misleadinglyunsuitable (Santos et al. 2012b).

In order to better discriminate the optimal climaticrequirements of different winegrape varieties (Jones et al.2005a), an innovative category analysis of the CompI is alsocarried out. At each site, the CompI is first classified as afunction 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. Thecalculations (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

Growing season precipitation (GSP)PSept:

AprilðPÞ mm - Blanco-Ward et al. 2007

P Precipitation (mm)

Huglin index (HI)PSept:

April

T�10ð Þþ Tmax�10ð Þ2 d °C > 900 Huglin 1978

T Mean temperature (ºC)

Tmax Maximum temperature (ºC)

d Length of day coefficient

Hydrothermal index (HyI)PAug:

AprilT � Pð Þ °C

mm<7,500 Branas et al. 1946

T Mean temperature (ºC)

P Precipitation (mm)

Dryness index (DI)PSept:

AprilWoþ P � Tv� Esð Þ mm >−100 Riou et al. 1994

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 previousmonth

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the leading HI category is identified an assigned to the site.HI values below 900 °C are considered unsuitable for wine-grape growth while values above 3,000 °C are relatively rarein Europe (not shown). This analysis allows a variety-dependent assessment of the climatic change impacts onthe viticultural sector.

Model data

Climate models remain a valuable tool in climate changeassessments (Solomon et al. 2011), despite their widelyreported limitations (e.g. Knutti et al. 2008; Hulme andMahony 2010), and are used here as a source of informationabout future climate change, namely in the temperature andprecipitation fields in Europe.

Regarding the definitions of the selected bioclimatic indi-ces (Table 1), daily mean, maximum and minimum 2-m airtemperatures and daily precipitation totals are used in theircalculations. These four daily atmospheric variables wereextracted from datasets of 16 state-of-the-art transient experi-ments generated by the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com; van der Linden and Mitchell2009), in a total of 15 different Global Climate Model /Regional Climate Model (GCM/RCM) chains (for theECHAM5/COSMO-CLM combination, two ensemble simu-lations are used). Table 2 lists the 16model experiments, alongwith their acronyms and main references. Only these 16transient experiments provide records without data gaps inthe two selected future periods (2011–2040 and 2041–2070).However, they provide a sufficiently representative sample ofmodels, with different parameterisations, spin ups, physicsand modelling approaches, covering a large amount of theuncertainty inherent to numerical model simulations.

The A1B IPCC-SRES scenario (2001–2100), which corre-sponds to a moderate anthropogenic radiative forcing(Nakićenović et al. 2000), was used. Other SRES emissionscenarios, such as A2, are not available for all ensemblemembers and were therefore not considered for the presentanalysis. However, the A1B and A2 scenarios start to clearlydiverge in their emission pathways only from 2070 onwards(Nakićenović et al. 2000), later than the aforementioned twofuture periods.

Daily model data was also extracted for the 40-year base-line period (C20; 1961–2000) so as to validate/calibrate itusing an observational dataset. The daily station-based grid-ded dataset (E-OBS, version-4) from the EU-FP6 projectENSEMBLES (http://ensembles-eu.metoffice.com), providedby the European Climate Assessment & Dataset (ECA&D)project (http://eca.knmi.nl) in the baseline period, was used forthis aim. The original gridded data is defined over land areas at201 grid boxes along latitude and 464 grid boxes alonglongitude (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). Detailedinformation about the E-OBS dataset can be found inHaylock et al. (2008). The baseline period (1961–2000) cor-responds to the longest available common time-seriesamongst the different datasets (observations and simulations).

All model datasets were bi-linearly interpolated (in lati-tude and longitude) from the original rotated grids (cf. theiroriginal spatial resolution in Table 2) to a regular grid of0.25° latitude×0.25° longitude to enable model validation/calibration using exactly the same grid as in E-OBS. Onlythe geographical sector [35-60°N; 12°W-36°E] where viti-cultural zoning is relevant is considered herein (102 gridboxes along latitude×190 grid boxes along longitude). Theselected bioclimatic indices were then computed for all gridpoints with available data (excluding blank/missing data) inthe E-OBS (11,506 grid cells). Statistically significant dif-ferences at a 99 % confidence level (using the two-sampleStudent’s t-test) between the future period of 2041–2070and the baseline period for HI, DI, HyI and CompI werealso computed and plotted.

Model output statistics and model uncertainties

Several studies have demonstrated that equal weights aregenerally a better approach than performance-based weightsfor computing ensemble mean statistics (Christensen et al.2010; Weigel et al. 2010). For this reason, only equallyweighted ensemble mean patterns for 2041–2070 (highergreenhouse gas forcing) are presented henceforth.However, an inter-comparison of model performances inreproducing the observed mean patterns of the selectedbioclimatic indices in the baseline period (1961–2000) wasundertaken in order to summarise and highlight the mostimportant deviations (bias) between simulated and E-OBSmean patterns. Scatterplots representing the different modelsas a function of their spatial mean bias (MB) and absolutespatial mean bias (AMB; less sensitive to extremes then theroot mean square deviation) in the HI, DI and HyI patternsare shown as supplementary material (Fig. S1).

Due to the model bias referred above, MOS are used to fitraw model data to observations, as the statistical distribu-tions of the simulated data present biases with respect toobservations (Wilks 2006). This is also a common proce-dure when assessing climate projections (Mearns et al.2001). Linear transfer-functions are applied to obtain trans-formed (adjusted) data with the same mean patterns as theobservational data. Taking into account the large number ofgrid boxes (11,506), higher order polynomial, exponentialor logarithmic transformations were not tested. This is not aclear shortcoming, mainly because the bioclimatic indicesare defined on a yearly basis and most of them are normallydistributed, according to the Lilliefors test applied to allindices and simulations (not shown); only a few exceptions

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were identified over some mountainous areas (e.g. theAlps).

The linear transformations were then applied to the year-ly bioclimatic indices, at each grid box, and for each modeldataset individually. The transfer-functions between indicescalculated from E-OBS and indices calculated from themodels were estimated for the 40-year baseline period(1961–2000). This scaling procedure has been used in pre-vious studies (Alexandrov and Hoogenboom 2000; Santoset al. 2012a; Fraga et al. 2012; Jakob Themeßl et al. 2011)and enables the correction of biases in the model simula-tions, also enabling a comparison between the differentRCMs. As such, for 1961–2000, the corrected data of eachindex are equal amongst the different simulations and equalto the corresponding E-OBS data. Therefore, for the base-line period, only results obtained using E-OBS are pre-sented. The same transfer-functions (coefficients) wereapplied to all datasets in the two future periods (2011–2040 and 2041–2070), leading to corrected patterns forfuture conditions. In this procedure, the temporal invarianceof the transfer-function between recent-past and future is a

basic underlying assumption. In any case, it must be kept inmind that the patterns of the mean differences between afuture period and the baseline period (climate change signal)are independent of these corrections. Also, applyingtransfer-functions to the indices and not to the originalclimatological data is a new methodology that may requirefurther research.

Measuring model uncertainty is also crucial when assess-ing future impacts of climate change. For this purpose, the16-member normalised interquartile range (third quartileminus first quartile divided by the mean at each grid point;NIQR hereafter) of the HI, DI, HyI and CompI is alsoassessed and plotted. The spatial correlations of the CompIbetween the 16 simulations and the ensemble mean are alsopresented so as to clarify the spatial coherence amongstthem.

Inter-annual variability

As previously stated, irregularity in the yields and winequality is an additional pitfall for the winemaking sector.

Table 2 Summary table of all Global Climate Model / RegionalClimate Model (GCM / RCM) model chains used in this study. Thecorresponding acronyms, institutions and original grid resolutions are

also listed. In all simulations the period 2011–2070 was used under theIPCC-SRES A1B scenario. References relevant to each chain are alsoindicated

Acronym GCM RCM Original grid Institution References

ARP Aladin(CNRM) ARPEGE-RM5.1 CNRM-Aladin 25 km CNRM Gibelin and Deque 2003

ARP HIRHAM(DMI) ARPEGE DMI-HIRHAM 25 km DMI Christensen et al. 1996

BCM RCA(SMHI) BCM SMHI-RCA 25 km SMHI Kjellström et al. 2005

Samuelsson et al. 2011

EH5 RACMO(KNMI) ECHAM5-r3 KNMI-RACMO2 25 km KNMI Lenderink et al. 2003

EH5 CLM1(MPI) ECHAM5-r1 COSMO-CLM-1 18 km MPI-M Böhm et al. 2006

Steppeler et al. 2003

EH5 CLM2(MPI) ECHAM5-r2 COSMO-CLM-2 18 km MPI-M Böhm et al. 2006;Steppeler et al. 2003

EH5 HIRHAM(DMI) ECHAM5-r3 DMI-HIRHAM 25 km DMI Christensen et al. 1996

EH5 RCA(SMHI) ECHAM5-r3 SMHI-RCA 25 km SMHI Kjellström et al. 2005

Samuelsson et al. 2011

EH5 RegCM(ICTP) ECHAM5-r3 ICTP-RegCM3 25 km ICTP Elguindi et al. 2007

Pal et al. 2007

EH5 REMO(MPI) ECHAM5-r3 MPI-REMO 25 km MPI-M Jacob and Podzun 1997

Jacob 2001

HC CLM(ETHZ) HadCM3Q0 (normal sens) ETHZ-CLM 25 km ETHZ Steppeler et al. 2003

Jaeger et al. 2008

HC HadRM3Q0(HC) HadCM3Q0 (normal sens) HC-HadRM3Q0 25 km HC Collins et al. 2011

HC HadRM3Q16(HC) HadCM3Q16 (high sens) HC-HadRM3Q16 25 km HC Collins et al. 2011

HC HadRM3Q3(HC) HadCM3Q3 (low sens) HC-HadRM3Q3 25 km HC Collins et al. 2011

HC RCA(SMHI) HadCM3Q3 (low sens) SMHI-RCA 25 km SMHI Kjellström et al. 2005

Samuelsson et al. 2011

HC RCA3(C4I) HadCM3Q16 (high sens) C4I-RCA3 25 km C4I Kjellström et al. 2005

Samuelsson et al. 2011

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Significant alterations in the inter-annual variability couldlead to large economic impacts for this perennial crop. Theratios between the averages of the 16 inter-annual standard-deviations (calculated for each of the 16 ensemble membersseparately) in 2041–2070 and 1961–2000 of the bioclimaticindices (HI, DI and HyI) are computed to assess changes ininter-annual variability.

Results

Recent-past viticultural zoning

The HI has been shown to be an effective tool for viticulturalzoning and has been thus widely applied (Jones et al. 2010). Itsensemble mean pattern (Fig. 1a) is highly coherent with theGSS pattern (not shown) and highlights the fact that large areasof southern and central Europe are suitable for winegrapegrowth, whereas regions northwards of the 53°N parallel aregenerally unsuitable. In this context, it should also be emphas-ised that different classes of the HI are related to grapevinevarieties 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 coolerclimates; Duchene and Schneider 2005).

The mean patterns for DI and HyI (Fig. 1b,c) give oppo-site perspectives of the humidity requirements for optimalgrapevine development. Severe dryness (assessed by the DI)and excessive humidity (assessed by the HyI) during thegrowing season commonly have detrimental impacts on thedifferent stages of the grapevine development (Branas et al.1946). Overall, the DI pattern depicts a remarkable contrastbetween southern Europe and central and northern Europe,and shows that dryness might be a limitation only over smallareas of southern Europe and in certain years (valuesbelow −100 mm), whilst excessive humidity levels (HyIabove 5,100 °Cmm) are generally restricted to Atlanticcoastal areas or to mountainous areas, such as the Alps orthe Carpathian range. The baseline period mean pattern forthe CompI (Fig. 1d) demonstrates this index’s usefulness inviticultural zoning, since it is coherent with the spatialdistribution 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 isan attempt to characterise the most relevant atmosphericrequirements for winegrape growth with a single index.According to our analysis, areas characterised by values ofthe CompI in excess of 0.5 (50 % of optimal years) in theperiod 1980–2009 encompass the most worldwide famous

winemaking regions in Europe (circles in Fig. 1e), which arelocated mostly over southern and central Europe, particular-ly in countries such as France, Italy, Spain, Portugal andGermany). This attests to the utility of this index forEuropean viticultural zoning. The more recent period1980–2009 was chosen because it more closely reveals thepresent time atmospheric conditions reflected in currentwinemaking regions. As E-OBS data, used for model cali-bration, is available for 697 out of a total of 754 winemakingregions, 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 equalto or above 0.5, i.e., are in agreement with the CompIpattern.

Future viticultural zoning

The HI patterns for the future period (Fig. 2a) shows anorthward displacement and a stronger increase over south-ern and Western Europe. This reveals an apparent northwardextension of the high suitability areas for 2041–2070. Infact, new suitable regions for grapevine development withinthe latitude belt 50-55°N are projected to arise, which is inline with the results obtained for GSS (not shown).Furthermore, important changes over southern Europeshould also be expected under anthropogenic radiativeforcing.

For the DI in the future period (Fig. 2b) there is asignificant drying over most of the southern half ofEurope, which is in agreement with the projected changesin the GSP (not shown). These changes are likely to yieldsevere dryness (DI<−100 mm) in areas such as southernIberia, Greece and Turkey. On the other hand, the projectedchanges in the HyI (Fig. 2c) reveal an enhancement of thehumidity levels over central and eastern Europe, explainedlargely by the joint effect of warmer and moister conditionsin the future (increase in the GSP and in the growing seasontemperature). Therefore, while dryness may represent athreat/challenge for winegrape growth in southern Europe(e.g. harmful water-stress), excessive humidity in centraland eastern Europe can potentially trigger pests and diseasesin 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 southernEuropean regions, especially due to the increased drynessin these areas. Conversely, large areas of central andWestern Europe are projected to become more suitable forviticulture, due to the more favourable thermal conditions.

Climate change signals and uncertainties

When assessing climate change projections, it is importantto 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 HydrothermalIndex (HyI) and d Composite Index (CompI) for the baseline period(1961–2000). e European wine regions (circles) with thecorresponding CompI value (cf. scale) for the period of 1980–2009

using the E-OBS data. Source of the wine regions locations: WineRegions of the World—Version 1.31 (URL: http://geocommons.com/overlays/3547)

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the respective model uncertainties (Deser et al. 2012). Thespatial correlations for the CompI pattern among the 16-members and their mean (Table S1) shows that most modelsare clearly inter-correlated, as well as with their ensemblemean (correlation coefficients above 0.7). The modelsshowing higher correlations with the ensemble mean arethe ARP HIRHAM (DMI), EH5 REMO (MPI), HC CLM(ETHZ) and HC HadRM3Q0 (HC). On the other hand, themodels 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. Althoughtheir spatial patterns may show important differences, theyare considered as equally probable in the present study(equal weights in the statistical measures).

To assess the ensemble variability in the future projec-tions (uncertainty) for the CompI in 2041–2070, some sta-tistical measures [means, medians, minima, maxima, NIQRand total ranges (TR)] are provided in Table 3 for a selectionof well-known grapevine growing regions throughoutEurope. The means reveal CompI values always higher than0.80 but for two regions (Alentejo-Borba, Tokaj-Hegyalja;cf. Fig. 2d), the medians are consistently higher than themeans, which reflects the negative skewness of the distri-butions (CompI upper limit of 1.0). The minima show a

large variability, with values ranging from 0.00 (Tokaj-Hegyalja) up to 0.99 (Champagne), while the maxima arealways 1.00, with only two exceptions (Alentejo-Borba,Tokaj-Hegyalja). Regarding the ranges of variability, sinceTR is more affected by extremes than NIQR, some discrep-ancies between them are apparent; a higher TR does notnecessarily imply a higher NIQR or vice-versa. ConsideringTR as an uncertainty measure, some regions reveal morepronounced uncertainties (Rheinhessen, Ribera del Duero,Chianti, Porto/Douro, Barolo, La Mancha, Alentejo-Borba,Tokaj-Hegyalja), whereas others present relatively lowuncertainties 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 2041–2070and 1961–2000) are now presented, along with thecorresponding NIQR (Fig. 4). The latter is adopted as ameasure of the model uncertainties. The significantincreases in the HI values over most of Europe (left panelFig. 4a) is associated with a relatively low uncertainty (rightpanel, Fig. 4a), with the exception of northern Great Britainand the Alps. Conversely, the changes in the DI pattern (leftpanel Fig. 4b) show large uncertainties, particularly in theregions where changes are most pronounced (theMediterranean basin; right panel Fig. 4b). The HyI shows

Fig. 2 As Fig. 1 but for the future period of 2041–2070 under the A1B IPCC-SRES scenario

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significant increases in eastern, northern and central Europe,while important decreases are found over southern and westernEurope (left panel Fig. 4c). For this pattern, low uncertaintylevels are displayed all across Europe (right panel Fig. 4c).Contrary to the DI, this index combines temperature withprecipitation and its lower uncertainty is thereby also influ-enced by temperature. The CompI undergoes significantincreases over large areas of northern, eastern and centralEurope, whereas decreases can be found in some areas ofsouthern Europe, mainly southern Iberia, southern Italy andGreece (left panel Fig. 4d). This index reveals high uncertain-ties in many eastern European regions (right panel Fig. 4d),

where the strongest climate change signal is found, changingfrom recent-past climate conditions mostly unsuitable for viti-culture to much higher suitability in the future. In these circum-stances, slight differences in the climate conditions and/orprojections tend to become very significant, in relative terms.

Categorisation of CompI

The CompI was analysed with respect to three relevantclasses 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

Fig. 3 a Composite index (CompI) of the more severe model [HC RCA3 (C4I)]. b CompI showing the less severe model [HC RCA (SMHI)], forthe time period 2041–2070

Table 3 Ensemble means, medians, minima, maxima, normalisedinterquartile ranges (NIQR) and total ranges (TR) of the CompositeIndex (CompI) in 2041–2070 and for a selection of European famous

winemaking regions (the respective grid box coordinates are alsolisted). Regions are ranked in ascending order with respect to theirTR values

Country Region Longitude/ latitude (º) Mean Median Minimum Maximum NIQR TR

France Champagne 4.003/ 49.155 0.98 1.00 0.93 1.00 0.03 0.07

France Coteaux du Loire −0.846/ 47.368 0.99 1.00 0.90 1.00 0.02 0.10

France Bordeaux −0.055/ 45.012 0.96 0.97 0.90 1.00 0.10 0.10

Germany Mosel 6.734/ 49.765 0.95 0.97 0.87 1.00 0.08 0.13

Spain Rioja 2.402/ 42.493 0.96 1.00 0.78 1.00 0.05 0.22

Germany Rheingau 7.944/ 49.988 0.96 1.00 0.77 1.00 0.03 0.23

Portugal Vinhos Verdes −8.545/ 41.570 0.94 1.00 0.73 1.00 0.07 0.27

France Alsace 7.6592/ 48.660 0.93 0.97 0.70 1.00 0.09 0.30

Germany Rheinhessen 8.254/ 49.936 0.94 0.97 0.63 1.00 0.05 0.37

Spain Ribera del Duero −4.465/ 41.609 0.94 1.00 0.55 1.00 0.05 0.45

Italy Chianti 11.040/ 43.640 0.90 0.97 0.52 1.00 0.12 0.48

Portugal Porto/Douro −7.555/ 41.170 0.92 1.00 0.47 1.00 0.05 0.53

Italy Barolo 8.545/ 41.570 0.86 0.93 0.47 1.00 0.15 0.53

Spain La Mancha 2.698/ 39.653 0.82 0.90 0.30 1.00 0.2 0.70

Portugal Alentejo-Borba −7.424/ 38.797 0.55 0.57 0.17 0.93 0.41 0.76

Hungary Tokaj-Hegyalja 21.349/ 48.187 0.71 0.87 0.00 0.97 0.13 0.97

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Fig. 4 Left panels Differences in the mean patterns (2041–2070minus 1961–2000), right panels NIQR (third quartile minus firstquartile 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, cHyI, d CompI

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three non-overlapping meaningful categories for viticulturalzoning in Europe. Each year at a given location is thuskeyed to one of these categories. The leading category ateach location and for each selected time period is thendetected and plotted. Only grid cells with at least 50 % ofoptimal years according to the total CompI (values equal toor higher then 0.5) were categorized.

For the baseline period (Fig. 5a), this pattern clearlyhighlights that approximately all potential winemakingregions north of the 45°N parallel are keyed to the firstcategory (associated with the HI first class, Fig. 5b). Atlower latitudes, the most important exception is verifiedover the northern Iberian Peninsula, where large areas arealso keyed to the first category; other minor exceptionsoccur over high altitude regions in southern Europe. Thesecond and third categories are widespread over southernEurope (nearer the Equator than 45°N), with low altitude(warmer) areas in the Mediterranean Basin depicting a pre-ponderance of the third category, such as in southwesternIberia, northern Italy (Po valley) and many coastal areas.

For both future periods (Fig. 5b, c) there is strong evidencefor new regions suitable for viticulture in different parts ofnorthern Europe (e.g. southern British Isles, the Netherlands,Denmark, northern Germany and Poland). In fact, the pro-jected changes in the number of suitable grid boxes (CompIgreater than 0.5) for 2041–2070 shows and increase polewardof the 50°N parallel (Fig. 6), though it gradually weakenstowards 60°N (upper latitude limit in the present study).Conversely, many regions in southern Europe (nearer theEquator than 41°N), such as southern Iberia, shift to unsuit-able conditions, which can be explained largely by the lack ofprecipitation and dryness (Fig. 2b). Within the latitude belt of41–50°N there is no remarkable change in the number ofsuitable grid boxes, though there are some very significantshifts amongst the three categories.

On the whole, new suitable regions northwards of 50°N arekeyed mainly, as expected, to the first category (Figs. 5, 6).Further, most regions from 46°N up to 50°N tend to changefrom the first to the second category, while many regionswithin 39–46°N are projected to change from the first orsecond category to the third category. Therefore, the firstcategory (the least heat demanding) is projected to be locatedessentially northwards of 50°N, as no new regions of thiscategory are expected to arise at lower latitudes. The secondand third categories undergo a northward displacement, leav-ing many current winemaking regions in southern Europewith unsuitable conditions, primarily because of the severedryness that is expected to prevail in their future climates.

Inter-annual variability

Due to the relevance of the inter-annual variability in viti-cultural zoning, corresponding climate change projections

are also analysed. As stated above, apart from a few excep-tions, the selected bioclimatic indices are normally distrib-uted and their inter-annual variability can thus be assessedby their sample standard-deviation. The map of the ratiosbetween the ensemble mean inter-annual standard-deviations of the HI for 2041–2070 and 1961–2000 revealsvalues significantly above one (higher variability) over largeareas of Europe, particularly over central Europe, the BritishIsles 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 someregions of central and northeastern Europe (Fig. 7b).

The DI inter-annual variability (Fig. 7c) also shows an up-ward trend, especially in central and eastern European regions,with larger values over Great Britain. The projected increases inprecipitation over these regions, combined with increased inter-annual variability, may become harmful to viticulture, withenhanced risks of pests and diseases. These results also reveallow model uncertainty (Fig. 7d). In addition to the increase inthe inter-annual variability of the HI and DI, the HyI showssignificant increases in its inter-annual variability over northeast-ern Europe (not shown). As such, there is evidence for an overallincrease in the inter-annual variability under future climates,which may also comprise an increase in the occurrence ofextremes, 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, particularlyover southern and western Europe (over 400 °C increase in HIfor 2041–2070). Further, dryness conditions in the growingseason (GSP and DI; Fig. 2b) are projected to undergo anenhancement over southern Europe, while humidity levels(GSP and HyI; Fig. 2c) are expected to remain relatively highover central and northern Europe. All these changes are alsocorroborated by the CompI, not only by its aggregate values(Fig. 2d), but also in relation to the different classes of the HIconsidered for category analysis (Figs. 5, 6).

These projections are consistent with other recent studiesby Neumann and Matzarakis (2011), for Germany, and byDuchene and Schneider (2005), for Alsace, France.Analogous changes in the mean patterns of HI (Fig. 2a) havealready been reported, including an increase of about 300units for six winegrowing European regions over the last30–50 years (Jones et al. 2005a). Additionally, under a futurewarmer climate, higher temperatures (above 30 °C) may ofteninhibit the formation of anthocyanin (Buttrose et al. 1971) andthus reduce grape colour (Downey et al. 2006). All thesechanges may imply the selection of new winegrape varietiesand a reshaping of the European viticulture.

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Analysis of the GSP and DI provides evidence of verydry climates over southern Europe (Fig. 2b). Although grape

quality is generally favoured by moderate water stress con-ditions during berry ripening (Koundouras et al. 1999;

Fig. 5 Leading categories inthe CompI (HI classes 1, 2 and3) for the baseline period (a)and the two future periods (b, c)

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Santos et al. 2003), severe dryness (DI below −100 mm) inmany Mediterranean-like climate regions may be damaging(Chaves et al. 2010); some of these regions are actuallyprojected to have a GSP below 200 mm (southern Iberia,southern Italy, Turkey and Greece). Changes in winemakingpractices 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

northern Europe, however, pests and diseases can also be adrawback to wine production, since changing climate con-ditions may modify the complex interrelationships betweenvine, pest and disease development (Stock et al. 2005).

Regarding downy mildew, patterns of change in the HyIsuggest, at first sight, low risks of contamination in southernEurope, but moderate-to-high risk at higher latitudes, thelatter as a result of warm and wet conditions. Nonetheless,implications of a future increase in the HyI at the continentalscale may not be too dramatic, mainly because, in theemerging winemaking areas, where thermal conditions willgradually become favourable to wine production, the HyIwill remain below the maximum threshold of 7,500 °Cmmin most years.

Further, a projected strengthening of the inter-annualvariability in the HI and DI may result in additional con-straints for this perennial crop and for the winemakingsector as a whole, particularly in view of an increasingirregularity and unpredictability of yields and wine quality.

A reshaping of the European regions suitable for grape-vine growing is likely to occur, taking into account theprojections of CompI. Many regions throughout Europeare shown to be undergoing a change to a higher CompIcategory. As an illustration, some regions in France and

Fig. 6 Latitudinal differences (2041–2070 minus 1961–2000) in thenumber of grid cells equal to or above 0.50 in the CompI

Fig. 7 a, c Ratio between the averages of the 16 inter-annual standard-deviations (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

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

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Germany will in the future present similar values to thoseseen today in the Mediterranean Basin. A general decreasein the number of suitable regions below 39°N might also beexpected. This outcome is also supported by previous find-ings (e.g. Kenny and Harrison 1992; Jones et al. 2005b;Stock et al. 2005; Fraga et al. 2012). Overall, shifts inCompI classifications throughout Europe suggest modifica-tions in the suitability of a given region to a specific wine-grape variety and are expected to influence wine yield/quality attributes.

The assessment of the uncertainty associated with theclimate change projections is of great importance forinforming decision-makers. Following the recommenda-tions in Christensen et al. (2010) and Weigel et al. (2010)in our study, model uncertainty for viticultural zoning wasassessed by considering all members of the ensembles withequal weight.

Although the ensemble means shown in the current studyare in overall agreement with a previous study that employed asingle model for similar purposes (Malheiro et al. 2010), thusgenerally corroborating previous findings, future climate con-ditions can depend greatly on the model experiment at local/regional scales (Dessai and Hulme 2007). In fact, at thesescales, remarkable differences were found between single-model projections and the ensemble projections presentedhere, which plainly substantiate the current study.

The increase in CO2 concentration is expected to be bene-ficial for winegrape growth (Moutinho-Pereira et al. 2004;Goncalves et al. 2009; Bindi et al. 1996). Reflecting thesettings of the ENSEMBLES project (van der Linden andMitchell 2009) in this study we considered only the A1Bemission pathway. Amore pronounced increase in atmospher-ic CO2 concentration is predicted by the A2 emission scenar-ios, especially relevant to the second half of the century, withimplications that need to be examined in future studies.

The impacts of the aforementioned changes in climatesuitability for the winemaking sector in Europe, can besummarized as follows: (1) some southern regions (e.g.Portugal, Spain and Italy) may face detrimental impactsowing to both severe dryness and unsuitably high temper-atures; (2) regions in western and central Europe (e.g.southern Britain, northern France and Germany), some ofwhich are world-renowned winemaking regions, might ben-efit from future climate conditions (higher suitability forgrapevine growth and higher wine quality); and (3) newpotential winegrape growth areas are expected to arise overnorthern and central Europe, where conditions are currentlyeither marginally suitable or too cold for this crop. It is stillworth mentioning that the higher inter-annual variability(climate irregularity) in the future over most of Europemay lead to additional threats to this sector.

Furthermore, the shortening of the growing season result-ing 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 thetime periods and classes in which the bioclimatic indices arecommonly calculated. Since grapevine phenology and winequality were shown to be correlated with HI (Jones et al.2005a; Orlandini et al. 2005), this limitation can, to someextent, be overcome by adding new classes to the existingclassical bioclimatic indices, instead of modifying the timeperiods (difficult to achieve due to the extent of the regionsstudied). A similar approach was indeed carried out bySantos et al. (2012b) using a new low-limit class for theHI (900–1,200 °C).

Future changes in the viticultural zoning in Europe im-pose new challenges for the winemaking sector. These mod-ifications give clues to the development of appropriatestrategies to be taken by the winemaking sector to faceclimate change impacts. There have already been reportsof acclaimed winemaking regions that may become unsuit-able for premium wine production within the current century(Hall and Jones 2009). As the patterns discussed in thisstudy are available at a relatively high spatial resolution(25–30 km), regional climate change assessments are per-mitted, enabling the development of local adaptation andmitigation measures, drawn specifically for the winemakingsector. Changes in soil and crop managements, as well asgenetic breeding and oenological practices, are crucial for abetter adjustment between viticulture and future environ-ment. These measures need to be planned adequately andtimely by stakeholders, policymakers, and by the differentsocioeconomic sectors that are directly or indirectly influ-enced by the vineyard and winemaking activities.

Acknowledgements We acknowledge the ENSEMBLES project(contract GOCE-CT-2003-505539), supported by the EuropeanCommission’s 6th Framework Programme (EU FP6) for supplyingthe model datasets (http://ensembles-eu.metoffice.com/). We thankDr. Joaquim Pinto, at the University of Cologne, the German FederalEnvironment Agency and the COSMO-CLM consortium for providingCOSMO-CLM data. We also acknowledge E-OBS and the data pro-viders in the ECA&D project (http://eca.knmi.nl). This study wascarried out under the Project Short-term climate change mitigationstrategies for Mediterranean vineyards (Fundação para a Ciência eTecnologia - FCT, contract PTDC/AGR-ALI/110877/2009). This workis also supported by European Union Funds (FEDER/COMPETE -Operational Competitiveness Programme) - under the project FCOMP-01-0124-FEDER-022692. H.F. also thanks the FCT for providing aresearch scholarship (BI/PTDC/AGR-ALI/110877/2009).

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