4.1 1951–2010 European degree-day climatologies
In Figure 3, we show the mean annual values of the degree-day variables in the period 1951–2010. We excluded 2011 to keep the analysis on exactly six decades. HDD showed the expected latitudinal gradient and a west-to-east smooth increase: in Europe, the lowest values were in southern Spain (500–800), and the highest in Finland (4500). CDD showed a south-to-north decrease: from the Mediterranean region (up to 600 in Europe) to Scandinavia (<5 in the Arctic Circle). GDD and WI showed a clear latitudinal pattern and the highest European values in the Mediterranean and the Black Sea regions (GDD close to 4000 and WI to 3000).
To analyse the last decade, we show the anomaly grids computed as the mean annual values in the period 2001–2010 minus the mean annual values in the period 1971–2000 (Figure 4). Compared to the period 1971–2000, the period 2001–2010 was characterized by lower HDD and higher CDD, GDD, and WI across almost the entire Europe. In particular, HDD showed the largest decrease in central Sweden, Finland and Russia and a small increase in Latvia and Turkey. Regarding CDD, the increase was more evident in the Mediterranean region and Turkey. Regarding GDD and WI, the western Mediterranean experienced the most evident increase.
In Section 'A simple model that allows degree-days to be obtained using only monthly data', we affirmed that the MAE of our modelling technique is low on station basis for HDD, GDD, and WI, and low but improvable for CDD. We computed the gridded discrepancies between degree-day variables calculated by means of TN–TX daily values and by our model applied to monthly TM in the test period 2001–2010. In Figure 5, the positive values mean that our model overestimated the degree-days computed with daily data. Regarding HDD, the differences exceeded 1% only in Croatia, Bulgaria, Slovakia, and Turkey; regarding CDD, the differences were smaller in absolute values, but up to 11% in Spain, the Republic of Macedonia, Serbia, and Turkey. If we skip the grid points with CDD < 5 and the extra-European territories, where the density of input data is low, the overall MAE for CDD reduced from 5.7 to 3.1%.
Figure 5. Gridded comparison (for 2001–2011) between HDD (left) and CDD (right) calculated with the daily dataset and with the model based on monthly TM.
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It seems that our model, based on TM, slightly overestimates HDD and CDD in coastal regions, in particular on the Mediterranean Sea (excluding Italy) and on the Black Sea. The model failed to account for the mitigating sea effect, which characterizes the first 15–20 km from the coastlines and involves in particular TN in winter and TX in summer. In contrast, our model slightly underestimated HDD and CDD in the inland territories (especially Bosnia-Herzegovina, Bulgaria, and Slovakia), due to a continental climate effect characterized by cold winters and hot summers. Also the Po Valley is biased, probably because the urbanization, combined with the stagnation of air masses and the absence of winds, causes local summer heat waves (very high TX). The next versions of the model will include a special parameterization for the coastal areas.
4.2 European and regional degree-day trends
To investigate the evolution of degree-days from 1951 to 2010, we performed a linear trend analysis. The statistical significance of each trend has been tested with a Student's t-test (Gosset, 1908) and the values are reported in degree-days year−1.
Figure 6 shows the gridded linear trends of HDD, CDD, GDD, and WI. HDD decreased almost everywhere in Europe excluding Iceland, northern Scandinavia, the Balkans, and Turkey, where HDD increased. For CDD, GDD, and WI, the Mediterranean region experienced the largest positive trends in the Provence (France) and Sardinia (Italy); GDD and WI showed significant positive trends in the majority of Europe, though the Baltic Sea region was characterized by negative trends, but the normal values were so small there (GDD ≈ 800, WI ≈ 450) that the crop cultivation was rarely possible.
Figure 6. Maps of degree-day trends in 1951–2010 at 95% confidence level. Grey: not significant trends; white: trends close to null values; black: no valid data.
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According to geographical and political features, we divided Europe into 14 regions. For each region and variable (Table 3), we calculated a 60-year record computed as the mean value amongst all the grid points located within the corresponding borders.
Table 3. Linear trends of HDD, CDD, GDD, and WI in degree-days year−1 for the period 1951–2010.
|No.||Region||ELEV||HDD year−1||CDD year−1||GDD year−1||WI year−1|
|1||Iceland||518.5|| || ||1.9 (99)||0.7 (99)|
|2||UK + Ireland||148.8||−2.7 (99)||0.1 (99)||3.8 (99)||2.7 (99)|
|3||Iberian Peninsula||639.1||−2.8 (99)||1.7 (99)||5.7 (99)||4.6 (99)|
|4||France + Belgium + Netherlands||245.6||−3.9 (99)||1.0 (99)||6.3 (99)||5.3 (99)|
|5||Alps||1370.3||−3.9 (99)||0.9 (99)||5.2 (99)||4.1 (99)|
|6||Germany + Denmark||263.8||−4.1 (99)||0.5 (99)||4.8 (99)||3.7 (99)|
|7||Scandinavia||331.3||−4.1 (95)||0.1 (99)||2.5 (99)||1.6 (99)|
|8||Italy||390.8||−2.8 (99)||1.9 (99)||6.2 (99)||5.3 (99)|
|9||Balkans||536.7||−2.4 (95)||1.1 (99)||4.4 (99)||3.5 (99)|
|10||Greece + Cyprus||431.5|| || || || |
|11||Carpathians||368.5||−3.6 (98)||0.6 (99)||3.5 (99)||2.7 (99)|
|12||Baltic Republics||136.1||−5.0 (99)||0.3 (99)||3.8 (99)||2.7 (99)|
|13||Black Sea||393.1|| ||1.1 (99)||3.8 (99)||2.8 (99)|
|14||West Russia + Ex-USSR||151.9||−5.9 (99)||0.1 (99)||2.8 (99)||1.8 (98)|
Almost every region showed a negative trend for HDD and a positive one for CDD, GDD, and WI, in accordance with the temperature rise in Europe, which is the main driving factor for degree-day trends. The negative trend in HDD was largest in Russia and northern central Europe. Regarding CDD, the largest positive trends occurred in Italy and the Iberian Peninsula; regarding GDD and WI, they occurred in the western Mediterranean region, but also in the Alps, and this may open the way for new cultivations of crops at higher elevation. However, if GDD are too high due to hot and maybe dry summers, the crop growth can be affected negatively.
In general, the decrease of HDD was faster in the last three decades than in the entire period analysed, as well as the increase of CDD, GDD, and WI has been characterized by larger values in the period 1981–2010 than 1951–2010. However, a few regions have been characterized by trends with opposite signs in the periods 1951–1980 and 1981–2010 (Table 4). For example, regarding HDD, Iceland, Greece, and Cyprus showed a positive trend in 1951–1980 and a negative one in 1981–2010. In southeastern Europe, CDD decreased in 1951–1980 and increased in 1981–2010. Similarly, GDD and WI decreased in 1951–1980 and increased in 1981–2010 in the Iberian Peninsula, Italy, the Balkans, the Carpathians, Greece, and Cyprus.
Table 4. Linear trends of HDD, CDD, GDD, and WI in degree-days year−1 for the periods 1951–1980 and 1981–2010. Only the trends significant at 95% level are shown.
| || ||HDD year−1||HDD year−1||CDD year−1||CDD year−1||GDD year−1||GDD year−1||WI year−1||WI year−1|
|1||Iceland||6.3||−11.3|| || ||−2.4||8.4|| ||2.6|
|2||UK + Ireland|| ||−6.7|| || || ||8.4|| ||5.6|
|3||Iberian Peninsula|| || || ||2.7||−6.1||9.7||−5.6||8.7|
|4||France + Belgium + Netherlands|| ||−6.0|| ||1.4|| ||10.5|| ||8.8|
|5||Alps|| ||−5.8|| ||1.3|| ||8.5||−2.8||7.0|
|6||Germany + Denmark|| || || ||0.8|| ||6.9|| ||5.9|
|7||Scandinavia|| ||−11.8|| ||0.3|| ||7.0|| ||4.7|
|10||Greece + Cyprus||2.7||−4.2||−5.5||5.3||−8.0||10.3||−10.2||9.1|
|11||Carpathians|| || ||−1.7||1.9||−6.2||8.9||−7.2||7.9|
|12||Baltic Republics|| || || ||1.0|| ||7.9|| ||6.8|
|13||Black Sea|| ||−7.2||−1.6||4.3|| ||12.1|| ||10.6|
|14||West Russia + Ex-USSR|| || || ||1.2|| ||7.3|| ||5.8|
We defined a 15th region, the European Mediterranean coast: from Gibraltar to Antalya (Turkey), delimited by a belt of 50 km from the coastline, excluding the points with an elevation higher than 600 m. For this area, we computed the anomaly series of the degree-day variables using the mean annual value of 1951–2010 as the reference value (Figure 7). HDD showed a significant decrease only in the last 30 years and the anomalies of CDD, GDD, and WI turned from negative to positive values in the 1980s. Regarding the period 1981–2010, the trends were all significant at 99% level: −5.2 HDD year−1, 4.1 CDD year−1, 10.7 GDD year−1, and 9.7 WI year−1.
Figure 7. Anomaly series (vs normal 1951–2010 values) of HDD, CDD, GDD, and WI for the European Mediterranean coastal region.
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As our methodology does not account for Tu, climatologies and trends of GDD can be slightly biased in the regions involved by relevant summer heat waves. In fact, extremely high temperatures should not be considered as GDD as they are expected to cause a reduction of primary productivity, as it happened during the European heat wave of 2003 (Ciais et al., 2005).
Theoretically, an increase in CDD leads to an increase in energy consumption to cool the internal environments, whereas a decrease in HDD leads to a saving, because the heating systems need to be turned on fewer days. To evaluate if these effects were balanced, we defined the energy degree-days (EDD) as the sum of HDD and CDD. In Figure 8, we show the linear trends of EDD: regarding 1951–2010, a negative trend was found for central Europe, Russia, UK, Ireland, and Scandinavia; regarding 1981–2010, the negative trend was even larger in Iceland, Scandinavia, UK, Ireland, Northern France, Italy, and former Yugoslavia.
Figure 8. Maps of EDD trends in 1951–2010 (left) and 1981–2010 (right) at 95% confidence level. Grey: not significant trends; white: trends close to null values; black: no valid data.
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HDD and CDD are roughly comparable, because the energy related to CDD and HDD varies if the heating (cooling) systems are based on different technologies and also because buildings are usually heated (cooled) to different temperatures according to the use, the local laws, and personal comfort. However, from a climatological point of view, a rational use of the air conditioning and the heating systems may lead to a save in energy consumption due to climate change (Christenson et al., 2006). In the last three decades, the decrease in HDD was greater than the increase in CDD in Italy and former Yugoslavia. Also, in northern Europe HDD decreased and CDD showed values so small that there was no need to cool the environments in summer.
4.3 The Winkler Index applied to grapevines
Grapes are one of the most important crops in Europe: according to the official dataset of the Statistic Division of the Food and Agriculture Organization of the United Nations (FAOSTAT; website: http://faostat3.fao.org/faostat-gateway/go/to/home/E), in 2010 Europe produced 46.8% of the world supply of grapes, i.e. more than 31.4 billion tons, and the countries on the Mediterranean Sea produced 42.4%. In particular, Italy (2nd), Spain (4th), France (5th), Turkey (6th), Egypt (11th), and Greece (15th) were amongst the greatest producers (Figure 9).
The WI was first proposed and applied to grapevines by Amerine and Winkler (1944), but we used the improved version presented by Winkler et al. (1974), see Equations (7) and (8), and we adapted the classification used in California (whose climate is roughly similar to the Mediterranean climate) to Europe as follows: Region I (1000 < WI ≤ 1500), II (1500 < WI ≤ 2000), III (2000 < WI ≤ 2500), IV (2500 < WI ≤ 3000), and V (WI > 3000).
In Figure 10, we show the 1961–1990 (left) and the 2001–2010 (right) climatologies of WI. In Europe, the most important producers were those countries whose land fell into more than two categories (Spain, France, Italy, Greece, and Turkey) because many different types of grapevines could be cultivated there (Johnson and Robinson, 2007). Moreover, following WI, new areas became suitable for grapevines from 1961–1990 to 2001–2010 in Belgium, the Netherlands, Germany, Slovakia, Poland, and Belarus.
According to this preliminary analysis, WI proved to be a valid indicator to detect the countries suitable for grape growth, in fact the greatest producers in Europe in 2010 (Figure 9) were those countries where WI indicated ‘favourable’ conditions (Figure 10). To improve these results, we plan to use soil information, construct higher resolution grids to capture the local features (south or north-facing hill slopes), and investigate the correlation between WI records and the grape production at country level.