INFORMATION SHEET ON FUTURE CLIMATE AND IMPACTS IN THE RURAL CASE STUDY: TUSCANY,
ITALY
Summary
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Present and future climate changes have been investigated in Tuscany by means of four CIRCE climate model runs:
Météo France (CNRM), MPI-Med (MPI), IPSL-Glob (IPSL1), and Proteus (ENEA).
Over the next few decades (2021-2050) in Tuscany the annual maximum temperature is projected to increase on
average by +0.54°C per decade. The largest increases are projected in the eastern hilly and southern areas of the
region, especially during summer.
A slight decrease in annual precipitation is projected by 2021-2050. The highest reductions are projected in summer
(up to –13 mm/decade over the entire region), while in autumn precipitation tends to increase (+23mm/decade).
In the future time slice, wheat crop shows a general advancing of phenological stage with a progressive reduction of
the growing cycle by ~20 days across the region (more accentuated in the vegetative period). The fertilizing effect of
enhanced CO2 allows the crop to recover yield losses due to climate change (rising temperature and reduced rainfall)
although increasing inter-annual variability is projected.
A permanent increase in maximum temperature (by 1°C) during the peak season could lead to a sizable decline (up
to -9.7%) in the following year in the number of domestic tourists.
1. Introduction
Two previous information sheets have been
compiled for Tuscany region: the first focused
on observed climate of the region using selected
key climate indicators and the second one on the
impacts and vulnerability of the Tuscan rural
community to current climate hazards using a set
of biogeophysical and social indicators.
For current climate variability, the analyses
detected, in recent decades, an increasing trend
in maximum temperature and in the
duration/frequency of heat waves, while annual
rainfall showed a slightly decreasing trend.
In the Biogeophysical and social vulnerability
indicators information sheet it has been outlined
how climate may have damaging consequences
on the rural regional economy by, for instance,
affecting the growth and development of
important crops or reducing the comfort and the
attractiveness of the Tuscan countryside to
tourists.
This information sheet focuses on projected
climate change and its impacts in the Tuscany
rural case study. Future trends in temperatures
and annual precipitation have been analysed
according to the output from four CIRCE
climate models (Météo France-CNRM, MPIMed, IPSL-Glob, and Proteus-ENEA). Two of
the biogeophysical and social vulnerability
indicators (wheat yield and domestic tourist
arrivals) have been analysed under future climate
conditions as projected by the CIRCE project
climate model runs.
A final section highlights that in Tuscany
climate is not the only key driver of change, and
that the complexities of interdependencies
between biogeophysical, social, and climate
systems mean that future changes in non-climate
drivers and vulnerabilities could accentuate or
diminish future climate change impacts. The key
socio-economic indicators discussed include
population growth/density, GDP, and use of
resources (such as water and energy).
1
2 Future climate
Climate models
The CIRCE project climate model runs (Gualdi et al., 2011) are used to investigate present and future
climate changes in Tuscany. The models adopted in this analysis are: Météo France (CNRM), MPIMed (MPI), IPSL-Glob (IPSL1), and Proteus (ENEA) with a spatial resolution ranging from 25 km
(MPI) to 50 km (CNRM). The analysis was performed using only grid cells whose centroid overlays
the land surface of the region (Figure 1). Data consist of daily values of temperature and precipitation
covering the period 1961–2050. Beyond the year 2000, the SRES A1B emissions scenario
(Nakićenović et al., 2000) is applied in all models. The Mann-Kendall test (Mann, 1945; Kendall,
1975) was used to assess the statistical significance of trends.
Figure 1: Centroids of CIRCE climate models (CNRM, IPSL1, MPI, and ENEA) used in the analysis of the
Tuscany case study.
2
Maximum Annual Temperature
What is it?
Figure 2 shows annual maximum temperatures in Tuscany simulated by the four CIRCE models
(CNRM, ENEA, MPI and IPSL1), for the period 1961-2050. The black line represents the ensemble
mean. The green line represents the observed values of annual maximum temperature derived from the
E-OBS data set for the period 1961-2009 (Haylock et al., 2008). Table 1 presents the ensemble mean
values and trends simulated by the models for the present climate (1961-1990) and the mid-century
(2021-2050) as well as the differences between these periods.
Figure 2: Annual maximum temperature simulated by ENEA, IPSL1, MPI and CNRM averaged over the
Tuscany region for the period 1961-2050. The black line is the average of the four simulations (CNRM, ENEA,
MPI, and IPSL1). The green line represents present-day values derived from E-OBS for the period 1961-2009.
What does this show?
Annual maximum temperature is projected to
increase in the next decades by all four models
(Figure 2). During the period 1961-1990, annual
observed (E-OBS) maximum temperatures
ranged from 16.2°C to 18.4°C. Similar values
are hindcast by ENEA and MPI, while IPSL1
and
CNRM
substantially
underestimate
observations by ~4°C. In the last 20 years (19902009), MPI and ENEA projections slightly
underestimate (~1°C) E-OBS data. In the future
period, all models project a steep increase in
annual maximum temperature that is statistically
significant (p<0.05) over the entire region. On
average, the increase is +0.54°C per decade
during the period 2021-2050. The highest
increasing trends are projected by MPI and
ENEA
(+0.71
and
+0.58°C/decade,
respectively). The greatest warming is mainly
located over the eastern hilly areas (CNRM,
ENEA and MPI) and the south (IPSL1) of the
region, and during the summer (on average
+0.8°C/decade) over the entire region.
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Table 1: Mean values and trends of the key temperature variables in Tuscany, for the present (1961-1990)
and mid-century (2021-2050) periods, calculated as an ensemble mean of the four CIRCE climate models
(ENEA, IPSL1, CNRM and MPI). Long term changes were calculated as the difference between mid-century
and present values. Inter-model ranges are shown as the standard error between estimates from the four
models. The spatial distribution is briefly described in the column ‘Spatial/seasonal location’.
Variable
Present climate
(1961-1990)
Long-term
variation
(1961/19902021/2050)
Mid-century
(2021-2050)
Mean (°C)
Trend
(°C/decade)
Mean (°C)
Trend
(°C/decade)
Mean change
(°C)
15.3 ± 1.0
+0.01 ±
0.05
17.1 ± 0.9
+0.54 ± 0.06
1.8 ± 0.3
T min
6.2 ± 1.0
+0.04 ±
0.06
7.8 ± 1.0
+ 0.45 ± 0.04
1.6 ± 0.3
T mean
10.7 ± 1.0
+0.02 ±
0.05
12.4 ± 1.0
+0.49 ± 0.05
+1.7 ± 0.3
T max
Why is it relevant?
Higher temperatures combined with droughts are
the major climate hazards in a Mediterranean
environment. Annual maximum temperature is a
key climate indicator for many physiological
processes of plants and animals and human
activities. Increasing maximum temperature as
projected by the CIRCE models for the next
mid-century may represent in Tuscany a big
Spatial/seasonal location
Future Tmax increases over the entire
region, but more so in the eastern hilly
areas of Tuscany (CNRM, ENEA and
MPI) and the south of the region (IPSL1).
The lowest increases are projected along
the coast by all four models. On average,
summers show the highest increases
(0.8°C/decade) over the entire region.
Major future Tmin increases are
concentrated over south-eastern inner
areas of the region (CNRM, ENEA and
MPI) and along the coast (IPSL1). On
average, summers show the highest
increases (0.6°C/decade) over the entire
region.
The highest future Tmean increases are
located in the over south-eastern areas
(CNRM, ENEA and MPI), and along the
coast (IPSL1). On average, summer
shows the highest increases
(0.7°C/decade) over the entire region.
threat on crop production (e.g., wheat, olive,
grapevine, fruit and vegetables) and on
landscape changes, a key resource for the rural
tourism of the region. Moreover, the expected
increase in maximum temperatures may reduce
the comfort and attractiveness of the Tuscan
countryside and agriturismi-farmhouses to
tourists.
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Annual Precipitation
What is it?
Figure 2 shows total annual precipitation simulated by four CIRCE climate models (CNRM, ENEA,
MPI and IPSL1) for the period 1961-2050, averaged over Tuscany. The black line represents the mean
of the four models. The green line displays the observed values of annual precipitation derived from
the ENSEMBLES project data set (E-OBS) for the time period 1961-2009 (Haylock et al., 2008).
1800
E-Obs
1600
ENEA
IPSL1
Annual Precipitations (mm)
1400
1200
MPI
CNRM
Mean
1000
800
600
400
200
0
Year
Figure 2: Annual precipitation simulated by ENEA, IPSL1, MPI and CNRM averaged over the Tuscany region,
1961-2050. The black line is the mean of the four climate model runs. The green line represents the observed
values extracted from E-OBS for the period 1961-2009.
What does this show?
Hindcast data from ENEA and IPSL show
similar values and variability as the E-OBS data,
while MPI and CNRM tend to underestimate
precipitation
in
1961-90.
Precipitation
projections for Tuscany are characterized by
considerable inter-annual variability. On
average, cumulative precipitation ranges from
740 to 1087 mm in 1961-90 and from 725 to
1037 mm in 2021-2050. Over the entire period
(1961-2050), all models simulate slight
decreasing trends in annual precipitation (on
average 10 mm/decade). In the mid-century, the
highest reductions are projected in summer (on
average -13mm/decade), mainly concentrated in
the eastern areas of the region. In Contrast, in
autumn, all the models project increasing trends
(on average +23mm/decade) across the region.
Why is it relevant?
Although small, future variation in precipitation
amount and distribution could have widereaching impacts on agriculture and rural
activities in Tuscany. These variations can also
lead to a considerable reduction in water
resource availability with negative impacts on
several sectors (e.g., agriculture, industry,
tourism.) which rely on the same limited
resource. Moreover, seasonal rainfall variability
could affect crop management, such as the
timing of sowing, fertilization, and harvesting.
For instance, the projected increasing trends in
autumn may become a serious constraint to
farmers during the sowing phases of winter
varieties of wheat (one of the main crops of the
region). On the other hand, future decreases in
summer precipitation are expected to lead to
increasing use of irrigation (including crops
traditionally rainfed). Moreover, a major
exploitation of water resources could reduce the
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availability and quality of freshwater resources.
Finally, rural tourism could also be seriously be
threatened by drier and warmer conditions
reducing the attractiveness of the rural Tuscan
landscape to tourists.
3 Future impacts
Wheat Yield
What is it?
To evaluate the impact of prospected climate change on durum wheat, a mechanistic model (SIRIUS
Quality v.1.1, Jamieson et al., 1998) was used to estimate the phenology and the yield of the crop in the
future time-slice 2021-2050 under the A1B scenario. SIRIUS model requires as input daily values of
min/max temperature, precipitation and radiation. The model outputs were compared with the results of
the simulations in the 30-year period 1961-1990 (the reference period). To take into account the
uncertainty in climate projections, the simulations were performed using three daily time-step gridded
data-sets produced within the CIRCE project, namely ENEA, MPI and IPSL. The analyses were
limited to grid points with altitude lower than 700 m.a.s.l. (above which durum wheat is not usually
cultivated). A climatic criterion was adopted to identify the optimum sowing date: starting from
October 1st and no later than February 15th; sowing was matched when the mean temperature of five
consecutive days was 14°C or lower and rainfall was lesser than 2mm. Nitrogen fertilization was set at
90 Kg N ha-1 and split into two applications: 1/3 during tillering and 2/3 at shooting.
Figure 5: Box-Whiskers plots of the duration of durum wheat growing cycle (left) and annual yields (right) in
the periods 1961-1990 (baseline) and 2021-2050 considering only the effect of climate change (CC) and
increased CO2 concentration (CC + CO2)
What does this show?
Figure 5 represents the distribution of the duration
of the growing cycle and of the annual yield,
resulting from the ensemble of the SIRIUS
simulations using the three CIRCE climate models.
Progressive increasing temperatures in the future
time-slice results in, a general advancing of
phenological stage with respect to the baseline and
in a shorter inter-phase time. On average, cropgrowing cycles are progressively reduced by ~ 20
days over the region. In particular, the vegetative
period (sowing-anthesis) was shortened by 18 days
on average, whereas the reproductive phase
(anthesis-ripeness) was less affected (-1.3 days with
respect to the baseline). Despite the lower time for
biomass accumulation, yields are unchanged,
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mainly due to the fertilizing effect of enhanced CO2.
In fact, without considering the CO2 effect, yield
yields are reduced by -6.8%. However, future
durum wheat yields tend to be less stable. The interannual variability, expressed as the coefficient of
variation (CV) of the annual yield distribution,
increased from 8.9% in the baseline to 12.6% in
2021-2050.
Why is it relevant?
Durum wheat is a rain-fed crop that it is widely
cultivated over Tuscany. The major climatic
constraints to durum wheat yield under
Mediterranean environments are both high
temperature and drought that frequently occur
during the growing cycle of the crop (Porter and
Semenov, 2005; García del Moral et al., 2003). As a
consequence, the projected climate changes for this
region, in particular rising temperatures and
decreasing rainfall, may seriously compromise
durum wheat yields, thus representing a serious
threat to the cultivation of this typical crop. A
shortened growing season may be counterbalanced
by enhanced radiation and water-use efficiency
derived from increased CO2 air concentration.
However, even though not significant, the increased
inter-annual variability in yield could represent a
threat for farmers’ income and could induce them to
substitute the crop with more stable ones.
Domestic tourist arrivals
What is it?
Figure 4 shows the marginal effect of a temporary increase in maximum summer temperature reported by
Cai et al. (submitted) who investigated the empirical impact of climate conditions on tourist flows in
Tuscany. This study relies on an eight-year panel dataset with a high degree of spatial disaggregation
(Tuscany’s 254 municipalities), to analyse how international and domestic tourist inflows are affected by
weather conditions at key times of the year. The analysis focused on maximum temperature (which is more
likely to be perceived by tourists) at key time of tourist season (from June to September, Regione Toscana,
2008) applying two types of econometric setups: fixed effects estimation of a static specification and system
GMM (Generalized Method of Moments; Wooldridge, 2002) estimation of a dynamic model. The first
model assesses the potential effects of climate change on tourism flows to Tuscany based on observing how
the two have varied in a set of destinations. The dynamic version of the model was applied to compute both
the short-term and long-term elasticity of tourist inflows to changes in climatic conditions.
Figure 4: Marginal effect of a temporary increase in maximum summer temperatures on domestic tourist arrivals
with 95% confidence intervals. The predicted change in tourist arrivals is expressed on a logarithmic scale (Y axis).
The independent variable is represented by the number of years after year t.
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What does this show?
Domestic tourist inflows are quite responsive to
variation in the climate variables, while
international tourist arrivals seem substantially
insensitive to those variables. These two types of
visitors may seek different kinds of attractions, and
enjoy different degrees of flexibility in adjusting
their travel arrangements in response to climate
information. Figure 4 shows the marginal effect of a
temporary increase in maximum summer
temperature on domestic tourist arrivals using the
dynamic econometric model application. Applying
the static model, a 1°C increase in summer Tmax
has no significant effect on domestic tourist arrivals
in the current year, but gives a statistically
significant (p<0.05) reduction of 9.7% (95%
confidence interval: ± 7.5%) in the following year.
Conversely, applying the dynamic model, if
maximum temperatures are higher by 1ºC on
average in the summer of year t and subsequently
revert to their initial value in each of the following
years, this reduces domestic tourist arrivals by 3.4%
in year t, 7% in year t + 1, and 4.2% in year t + 2,
and 2.5% in year t + 3 (Figure 4).
4. Socio-economic trends
In Tuscany, climate is not the unique driver of
change: interdependences among biogeophysical –
social – economic patterns are very complex and
these interactions may alter (accentuating or
diminishing) the future vulnerability of the system
due to climate change impacts. Tuscany is one of
twenty Italian administrative regions (the fifth in
order of size), with a population of 3,677,048
inhabitants over 22,992 km2. The population density
is thus about 160 persons/ km2, slightly lower than
the national average. More than 50% of the regional
population is concentrated in cities located along the
Arno River basin. Despite a relatively constant
decline in the birth rate from the seventies onwards,
the population was quite stable until the end of the
nineties. The increase in immigration rates, both
from southern Italian regions and from foreign
countries (in particular Eastern Europe and Africa),
has produced a significant population increase in the
last 10 years (+5.1%) representing the main driver
of population growth. Currently, foreigners
represent 4.6% of the total population and this
percentage is projected to reach 12% by 2020.
The Tuscany GDP (96,890 Million Purchasing
Standards -as in Eurostat- €27,400.00 per capita)
Why is this important?
The importance of climate as a factor in tourist
destination choice has been widely acknowledged
(Scott et al., 2005). Many studies (Scott et al., 2004;
Hamilton et al., 2005; Amelung et al., 2007) state
that the projected climate change will likely shift
tourism flows toward higher latitudes and altitudes.
The direction of impact on tourist flows to Tuscany
of such climate modifications is not that clear. It is
conceivable that, even as maximum temperatures
rise above the levels perceived by tourists as
comfortable, visitation rates to key historic and
cultural destinations may not be affected much. At
the same time, it seems plausible that most other
parts of the region may exhibit the same type of
vulnerability to climate change as in many
Mediterranean regions (Perry 2000; Amelung and
Viner, 2004). In this context, the increasing
occurrence of extreme climate events, such as heat
waves, have the potential to significantly reduce
tourism flows to Tuscany, with outstanding
consequences for the Tuscan tourism industry
which accounts for 8% of regional GDP (Regione
Toscana, 2010).
was 6.72 % of the national GDP in 2006 and 0.83%
of the EU. The sector contribution to GDP in
Tuscany is shown in Figure 5. The import/export
balance for Tuscany is +€4,967 million.
Figure 5: Composition of GDP for Tuscany
(source ISTAT)
Water resources in Tuscany are currently sufficient
to meet the region’s demand. Currently, water
consumption is allocated as follows: 45% for
households, 34% for the secondary sector, and 21%
for agriculture and livestock. Analysis of the trend
in the recent years indicates a continuous increase.
This fact, coupled with the projected climate change
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impacts for the Tuscany region, will likely lead to a
conflict of interests on the water resources from the
different sectors, which reveals the need to deal
with water resources management.
The Tuscany region is characterized by a hilly and
mountainous landscape and by a lack of significant
surface water (rivers, reservoirs) for agriculture
irrigation. Traditionally the farming system is
consolidated mainly by crops, which are well
adapted to specific soil and climate conditions.
Variation in climate conditions might likely lead to
a reduction of crop yields which typically
characterize the Tuscan landscape. Water should be
carefully managed, avoiding waste and increasing
efficiency in all sectors, in order to preserve a
globally fundamental resource.
The energy demand in Tuscany increases by +2%
yearly, with a subsequent increase in energy deficit
(currently around 12%). In 2004 the total Tuscan
consumption was 21,000 Gwh (35% by the
industrial sector, 32% by household sector, 31.5%
by the transport sector and 1.5% y the agricultural
sector), while the local production was around
19,000 Gwh. Interestingly, 28% of the energy
produced in Tuscany is geothermal (thus
renewable). 30% of energy consumption is from
renewable sources, mainly hydroelectric. 77% is
produced by thermoelectric power plants, in
particular using oil (even if the conversion to a
combined cycle with methane has been recently
started). The regional law No. 39 of 2005 regarding
the energy sector, followed by the Regional
Energetic Plan (PIER, Piano di Indirizzo Energetico
Regionale), designed a new energy policy for
Tuscany, with €109 million invested to have, by
2020, 39% locally produced electricity, and 10%
thermal energy from renewable sources, reducing
CO2 emissions by 7.2 million tons.
5. Uncertainties
There are uncertainties present in all stages of
CIRCE case-studies, from observations, to
emissions, models, projections, impacts and
adaptive response. The main novelty of the CIRCE
project is the inclusion of a realistic representation
of the Mediterranean Sea in the climate models. To
reduce the uncertainties related to climate
projections a multi-model approach was undertaken.
Four climate models (Météo France-CNRM, MPIMed, IPSL-Glob, and Proteus-ENEA) developed
within the CIRCE project were used to assess
climate changes and impacts in Tuscany.
Although variability is present among models, all
the models show similar trends in future
projections, depicting progressive warming and
drying conditions for the region (see previous
sections). Some uncertainties persist concerning the
pattern of change across the region. For instance,
despite a substantial consensus among three out of
four models (CNRM, ENEA and MPI), IPSL1
shows different spatial distribution of future
temperature increases (see Table 1).
Another source of uncertainty is the impact models
used. For instance, crop growth models, although
mechanistic, may contain many simple empiricallyderived relationships that do not completely
represent actual plant processes. Several aspects,
such as weeds, pests, extreme climate events and
soil conditions (i.e., salinity or acidity) are not
considered or scarcely controlled for, representing
another source of uncertainty. Exploring such
uncertainties, considering the use of several impact
models, should be taken into account for future
investigations.
Concerning tourism, the model estimates the
"causal" effect of an average change in temperature
on tourism arrivals. Although summer Tmax is
related to extreme events that may greatly affect
tourist comfort, such as heat waves, very hot
days/nights, these latter are not directly taken into
account. It is clear that the approach applied is
better suited for estimating the direct effect (i.e.,
attractiveness of the destination due to ‘good’ or
‘bad’ weather) of varying climatic conditions than
for evaluating their indirect consequences, such as
those emerging through changes in amenities
(Simpson et al., 2008). For this reason, further
research should also consider indirect effects, such
as a decline in landscape attractiveness or reduced
water resources for tourism (e.g., swimming pools,
drinking water) and also for seasonal changes in
behavior of tourists (e.g., a shift from peak summer
season to shoulder seasons, or the potential benefits
of a lengthening of the summer season). In addition,
future work could consider the relative
attractiveness of specific areas of the region, since
coastal areas are projected to be affected by a lower
rate of temperature increases with respect to inland
ones.
6. Integrated assessment
Table 2 summarizes climate trends and changes in
the Tuscany rural case study based on the ensemble
of four CIRCE models for the present climate
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(1961-1990) and the mid-century (2021-2050)
periods. In the next few decades, the expected
increase in temperature, hot days/nights and heat
waves may have serious consequences for many
environmental processes and for human health and
therefore will likely make the system more
vulnerable with respect to the present. The
considerable inter-annual variability in precipitation
continues to affect crop management practises in
agriculture (e.g., sowing and harvesting) as well as
crop yields and yield quality. The decreasing trends
of precipitation expected in the mid-century are not
very high, but are mainly concentrated in the
summer season, lowering the availability and
quality of freshwater resources which could cripple
several sectors (e.g., tourism and agriculture).
when the fertilizing effect of increased CO2
concentration is also considered. On the other hand,
notable impacts are expected for tourism: the
reduction of annual domestic tourist arrivals related
to increases of summer Tmax could likely affect the
tourism economy of the region. Moreover, the
projected longer-lasting and more frequent hot
days/night and heat waves may in the next decades
cause considerable disruption of human activities
with negative social, economic and environmental
effects.
On this basis, policy makers are strongly
recommended to develop appropriate efficient
short/long-term strategies to cope with the expected
impacts of future climate on Tuscany that have been
highlighted throughout the CIRCE project.
Wheat, one of the main crops of Tuscany, shows no
relevant future trend due to variation in climate
Acknowledgements
CIRCE (Climate Change and Impact Research: the Mediterranean Environment) is funded by the Commission of the
European Union (Contract No 036961 GOCE) http://www.circeproject.eu/. This information sheet forms part of the
CIRCE deliverables D11.4.5.
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Author: Camilla Dibari (camilla.dibari@unifi.it)1, Roberto Ferrise (roberto.ferrise@unifi.it)1, Marco Bindi
(marco.bindi@unifi.it)2
1
Department of Plant, Soil and Environmental Science, University of Florence, Italy, 2IBIMET_CNR, Florence, Italy;
Editors: Maureen Agnew (m.agnew@uea.ac.uk) and Clare Goodess (c.goodess@uea.ac.uk), Climatic Research Unit,
School of Environmental Sciences, University of East Anglia, Norwich, UK
Date: July 2011
Table 2: Summary of annual trends and changes in Tuscany for the present 1961-1990 and the mid-century 20212050 periods based on a set of numerical simulations.
present (1961-1990)
Climate
Indicator
(hazard)
trends
T mean
(°C/decade)
+0.0
T max
(°C/ decade)
+0.0
T min
(°C/ decade)
+0.0
Precipitation
(mm/decade)
+5.2
Hot days
(n° days/decade)
+1.5
Hot nights
(n° days/decade)
+2.5
Heat waves
(n° of
days/decade)
Wheat Yield
(ton/ha/decade)
Domestic tourist
arrivals
impact
no
significant
change
no
significant
change
no
significant
change
small
increase
no
significant
no
significant
change
no
significant
change
mid-century
(2021-2050)
Trends
long-term
(1961-1990 to
2021-2050)
changes
impact
+0.5
+1.7 °C
air warming
+0.5
+1.7 °C
day air warming
+0.4
+1.6 °C
night air warming
-4.4
-64.3 mm
Increase in water
resource competition
+8.1
+27 days
Increase in number of
very hot days
+8.9
+33 days
Increase in number of
very hot nights
+0.0
no
significant
change
+8.6
No trend
no
significant
change
No trend
1ºC increase in summer Tmax temperatures
reduces annual arrivals of tourists originating
from Italy by 9.7% (95% confidence interval: ±
7.5%) in the following year
+26.8 days
-
Significant
(p<0.05)
Increase in maximum
number of consecutive
days with Tmax higher
than long-term (19611990) Tmax 90th
percentile.
Stable wheat yield
considering the effect
of climate change with
increased CO2
concentration
Potential reduction in
domestic tourist
arrivals
11