Global and Planetary Change 63 (2008) 185–195

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Global and Planetary Change

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Climate variability in north-western Italy during the second half of the 20th century

N. Ciccarelli a, J. von Hardenberg a,⁎, A. Provenzale a, C. Ronchi b, A. Vargiu b, R. Pelosini b

a ISAC-CNR, Corso Fiume 4, I-10133 Torino, Italyb ARPA Piemonte, Corso Unione Sovietica 216, 10134 Torino, Italy

⁎ Corresponding author.E-mail address: j.vonhardenberg@isac.cnr.it (J. von H

0921-8181/$ – see front matter © 2008 Elsevier B.V. Aldoi:10.1016/j.gloplacha.2008.03.006

A B S T R A C T

A R T I C L E I N F O

Article history:

We analyse a large set of da Accepted 20 March 2008Available online 9 April 2008

Keywords:Climate variabilityTime series analysisImpacts of climate changeRegional studies

ily temperature and precipitation time series measured by a dense observationalnetwork in north-western Italy, in the period from 1952 to 2002. We find that average temperatures display asignificant increase of about 1 °C over the period of observation. The increase is particularly evident formaximum daily temperatures in winter and summer months. By contrast, precipitation time series displayno significant trend over the last fifty years. We also determine the statistical properties of interannualfluctuations in temperature and precipitation, and quantify their correlation with large-scale atmosphericpatterns and global indices such as the North Atlantic Oscillation (NAO), the Scandinavian pattern (SCAN) andEuropean Blocking (EB). The positive phase of the Scandinavian pattern and the presence of frequentblocking episodes are found to be significantly correlated with increased summer and fall precipitation andcold temperatures in the study area.

© 2008 Elsevier B.V. All rights reserved.

1. Introduction

The European Alps and their foothills are characterized by a ha-zardousmixture of potentially severe hydrogeological risk and intenseantrophic pressure, that call for a quantitative assessment of thepossible impact of climate change (Watson et al., 1998).

As a first step in estimating possible future changes and their im-pacts, it is necessary to carefully determine the current and past climaticvariability and the significance of climatic trends by a rigorous statisticalanalysis of the available data. From these, one could assess the impact ofclimate change by adopting empirical statistical models that considerthe specific response of local sub-systems to climatic variability, see e.g.Jacobson et al. (2004) for the case of an alpine ecosystem.

In past years, detailed national and regional datasets have beencompiled (Auer and Böhm,1994;Widmann andSchär,1997; Begert et al.,2005). More recently, much effort has been devoted to the constructionof large, homogenized, international datasets of observed temperatureand precipitation (Frei and Schär,1998; Böhmet al., 2001; Schmidli et al.,2002;Aueret al., 2005;Castyet al., 2005). In thesedatasets covering largeareas, the density of the measurement network is necessarily limited. Inalpine areas, however, high mountains create barriers to large-scaleatmospheric flows and intercept precipitation, leading to a particularlycomplex and spatially-variable climate (Beniston, 2005; Casty et al.,2005). Topographic control of precipitation, combinedwith the differentexposure of mountain flanks and the presence of several narrow valleys,leads to a wide range of different local climatic conditions. Dense and

ardenberg).

l rights reserved.

homogeneous regional observational networks are thus needed tocapture the average climate variability and to reduce possible biasesgenerated by the behaviour of individual measurement stations.

In this work we present the analysis of a new, recently digitized,large dataset of daily temperatures and precipitation in the Piedmontand Valle d'Aosta regions in north-western Italy, obtained from adense and uniformly distributed regional measurement network ac-tive during the second half of the 20th century. This dataset was neverpublished in its entirety before.

North-western Italy is exposed to the inflow of moisture-rich flowsfrom theMediterranean and to topographically-induced precipitation.A combination of these factors has led in recent years (1994 and 2000)to severe flood events (Buzzi et al., 1998; Turato et al., 2004). Theprecipitation climatology of this areawas already described in 1911 byAnfossi, and the overall geographical properties of cumulated annualrainfall in the last century in Piedmont have been described inBiancotti et al. (1998). A few individual daily precipitation time seriesfrom this region have been thoroughly analysed (Di Napoli andMercalli, 1996; Anfossi et al., 2002). Data from a few stations in thisregion were part of more extensive studies of precipitation over theentire northwestern Mediterranean (Piervitali et al., 1998), over Italy(Buffoni et al., 1999; Brunetti et al., 2000b) and in the Alps during thepast century (Frei and Schär, 1998; Schmidli et al., 2002; Brunetti et al.,2006). Analysis of temperature variability in this region has, until now,been limited to time series from a few stations, included in larger-scale studies (Brunetti et al., 2000a,b; Böhm et al., 2001).

After discussing the new high-density precipitation and tempera-ture dataset, in the rest of this paper we focus on some of the mainaspects of climate change on regional scales, namely the investigation

Fig. 1. a) Location of the region considered in this study (in gray). b) Geographicaldistribution of the measurement stations. Circles: location of temperature gauges;Crosses: location of precipitation gauges.

Fig. 2. Number of valid daily data in each year and for each station. a) temperaturemeasurements; b) precipitation measurements. The gray scale ranges from black (nodata in a year) to white (year is complete).

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of long-term trends, the analysis of interannual variability, the study ofspatial variability patterns obtained by Principal Component Analysisand the correlation between regional climate variability and large-scale circulation patterns. The results obtained from the analysis of thestation temperature data are compared with the ERA40 reanalysisdataset from the European Centre for Medium-Range Weather Fore-casts, available for the same period (Uppala et al., 2005).

2. Data and methods

2.1. Precipitation and temperature time series

We analyse time series of daily cumulated precipitation and ofdaily minimum and maximum temperatures, measured by a denseobservational network located in the Piedmont and in the Valled'Aosta regions in north-western Italy (Fig.1a). The network, shown inFig. 1b, includes 119 rain gauges and 40 thermometric stations, and itis managed by the Regional Agency for Environmental Protection

(ARPA) of the Regional Administration Piedmont and by the RegionalAdministration Valle d'Aosta, in Italy.

In recent years, the original networkwas gradually substitutedwith anew network of automatically-transmitting gauges, sometimes posi-tioned at slightly different locations. In this work we consider only datafrom the original network, focussing on the period 1952–2002, duringwhich a largenumberofmeasuringstations is available. All available timeseries were checked for internal consistency, for the presence of outliers,and to assure that periods of no station activity were not mistaken forperiods of no precipitation. Outliers and erroneous or suspicious datawere marked as missing and excluded from further analysis. Dry spellswere considered only if not interrupted or bracketed by missing data.

Fig. 2a and b report the number of measurements available in eachyear for each station for respectively temperature and precipitation.Some stations provided valid data only for a fraction of the entireperiod of interest and some years are incomplete. In the following, weavoid the use of temporal interpolation techniques to fill these gapsand analyse only the measured data. Data in a year or in a season areused only if the number of missing days is not too large; annual andseasonal averages are computed only if no more than 31 days in a yearand/or 7 days in a season are missing. Appropriate statistical tech-niques, described below, are used to estimate the sampling error andto test against the possibility that the results are affected by thetemporally inhomogeneous data availability.

Long-term averages, over the entire period, of daily maximum andminimum temperatures are significantly correlated with station eleva-tion, while temperature variances do not display significant correlation

Fig. 3. Ensemble-averaged precipitation statistics. a) Average precipitation; b)Precipitation intensity; c) Percentage of dry days in a year. Gray bands indicate 95%confidence bands obtained by a jack-knife subsampling technique.

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with elevation. Precipitation time series display a dependence onstation elevation both in mean and variance. For these reasons, wereduce the presence of possible biases in ensemble averages over theset of measurement stations by considering (a) temperature anomalies,T Vj (d, y)=Tj(d, y)− T̄ j, obtained by subtracting from each temperature timeseries, Tj(d, y), the long-term station average, T̄ j, where d is the day of theyear, y is the year and j labels the station; and (b) standardizedprecipitation time series, pjs(d, y)=p Vj (d, y)/σj obtained by dividing preci-pitation anomalies, p Vj (d, y)=pj(d, y)− p̄j, by the square root of the 4 long-term precipitation variance at the station considered, σj. Hereinafter, by“ensemble averages”we mean averages taken over the set of measure-ment stations. Annual day counts and annual averages start on Decem-ber 1st: The data corresponding e.g. to the year 1987 go from December1st, 1986 to November 30th, 1987.

We also compare the results on temperatures from station datawiththose obtained from the ECMWF ERA40 reanalysis project (Uppala et al.,2005) spanning the period 1958–2002. This project provides a historicaldatabase of estimates of the physical state of the atmosphere, obtainedby variational assimilation of available observations into a global atmos-pheric circulation model. The reanalysis data are statistically indepen-dentof the stationdatausedhere as the latterwerenot part of theERA40assimilation cycle. We extract daily minimum and maximum surfacetemperature anomalies from the ERA40 dataset, using a grid resolutionof 1.25°, and we obtain a single timeseries by averaging data from the 9grid points covering the region of interest.

2.2. Monte-Carlo methods

In the following, we extensively use Monte-Carlo methods toestimate sampling errors and to assess the significance of the results.Some of these techniques will be detailed where necessary but two ofthem, described in the following, are at the base of our analyses.

The variable number of valid stations available in each year leads toan error in the determination of annual means which varies from yearto year. In order to assess this sampling error in time series of ensem-ble-averaged data, we obtain confidence bands using jack-knife sub-sampling without replacement (Efron, 1981): In each year we select arandom subset composed of half of the available stations and wecompute a new ensemble-averaged value. Repeating this operationmany times (1000) allows for defining 95% confidence bands shown asgray bands in the figures.

A Monte-Carlo method is used to assess the significance of lineartrends (Pollard et al., 1987). To test a time series against the nullhypothesis of no trend, we create a large number (1000) of time seriesby randomly shuffling the original time series. The trend of the ori-ginal time series is then compared with the distribution of the lineartrends obtained from the randomly-shuffled time series. The originaltrend is considered to be significant only if less than 5% of the trendscomputed from the randomized time series exceed it in absolute valuesense (two-sided test). We compared the results provided by thismethod with a standard Mann–Kendall test at the same significancelevel, obtaining the same results (apart one single exception men-tioned below).

3. Long-term trends

3.1. Precipitation

Working with daily data allows to define the following statisticsfrom the precipitation time series, all averaged on a yearly or seasonaltime scale: average precipitation, precipitation intensity (precipitationaveraged only over rainy days), the average duration of dry periodsand of rainy events in each year/season, the average cumulated preci-pitation in rainy events and the percentage of non-rainy days. Ex-tremes are analysed by computing also the longest duration of dryspells in a period, the longest duration of rainy episodes and maximaof cumulated precipitation per event in each year. For each station,these statistics are standardized to unit variance and zeromean beforefurther analysis. A standard definition of winter (DJF), spring (MMA),summer (JJA) and autumn (SON) is used. As mentioned above, annualaverages start in December. A standard threshold of 1 mm/day is usedto define a rainy day.

The ensemble-averaged time series of standardized annual averageprecipitation, average precipitation intensity and the percentage ofdry days in a year are shown in Fig. 3a–c respectively. From these timeseries, we compute linear trends by least-square estimates (linearregression), weighting each data point by the square root of thenumber of stations active in that year. Analysis of ensemble means ofall statistics defined above, both at the yearly and at the seasonal timescale, shows very small trends of the order of 10–3 standard deviationsper year. None of these trends is significant, as we verified using boththeMonte-Carlo technique illustrated above and aMann–Kendall test,both at a 5% significance level. In all cases the residuals of the linearregression present no heteroscedasticity and are found to besignificantly uncorrelated, following a standard Durbin–Watson test(Wilks, 1995). Similar results are obtained considering anomalies in-stead of standardized time series.

Even if the ensemble averages of precipitation variables do notshow any significant trend, these can instead be found for individualstation data: Fig. 4a–c show, for each station having more than25 years of valid data, the trends of standardized time series of averageprecipitation, precipitation intensity and percentage of dry days in ayear, as a function of station elevation. Overall, stations showing asignificant positive trend are offset by others showing significantnegative trends. No elevation-dependent pattern emerges from theseresults, as the stations with significant positive or negative precipita-tion trends are evenly spread in elevation. Note, as shown in Fig. 4d,that significant single-station trends of yearly average precipitation

Fig. 4. Linear trends over the full period 1952–2002 of data from stations that have at least 25 years of valid data (non-necessarily contiguous). Significant (non-significant) trends aremarkedwith full (empty) circles. Plotted as a function of station elevation: a) average precipitation; b) precipitation intensity; c) percentage of dry days in a year. Plotted as a functionof station latitude: d) average precipitation. The significance of linear trends was assessed as described in the text.

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show a dependence on station latitude. This suggests a trend in thespatial distribution of precipitation which we will analyze in furtherdetail in Section 6. Trends of precipitation intensity and of thepercentage of dry days in a year have no such dependence on latitude(not shown).

Fig. 5. Longest duration of a dry spell in each year and for each station (in days).

The results obtained with single-station data highlight the impor-tance of considering dense observational networks also over quitesmall regions such as that considered here. An analysis limited to one

Fig. 6. Annual mean temperature anomalies, ensemble-averaged over all stations.Upper curve: maximum temperatures; lower curve: minimum temperatures. Graybands indicate 95% confidence bands obtained by a jack-knife subsampling technique.

Fig. 7. Seasonally-averaged minimum and maximum temperature anomalies, ensemble-averaged over all stations. Upper curves: maximum temperatures; lower curves: minimumtemperatures. Gray bands indicate 95% confidence bands obtained by jack-knife subsampling. Panels a–d showwinter (DJF), spring (MAM), summer (JJA) and autumn (SON) monthsrespectively.

Table 1Temperature anomaly trends in °C/yearTable 1Temperature anomaly trends in °C/year

Significant trends are on a gray background. Significance is assessed as explained in the text.

189N. Ciccarelli et al. / Global and Planetary Change 63 (2008) 185–195

or a few stations is hardly representative of the overall regional trendand it could lead to spurious results.

Another observation is that even if no significant trend is as-sociated with the longest duration of a dry spell in a year, this statisticshows extremely strong fluctuations, especially in recent years, asshown in Fig. 5. This plot shows the maximum duration of dry spellsfor each station and each year, and it indicates that frequent episodeswith very long dry spells are evident, particularly starting from 1989.

Until now, studies on precipitation trends which included our areaof study as a subregion have led to a somewhat controversial picture,which can be due to either the limited number of stations included insome of these studies and/or consideration of a different temporalwindow. Weak or non-significant trends for precipitation in the pastcentury have been found at the Mediterranean scale (Norrant andDouguedroit, 2006), in the Alpine region (Beniston, 2005) and fromthe analysis of individual time series (Anfossi et al., 2002). While asignificant increase inwinter precipitation inwestern Switzerland hasbeen reported (Widmann and Schär, 1997), a more recent analysis(Schmidli et al., 2002), while confirming this result, has identified adecrease of winter and autumn precipitation to the South of the mainridge, in an area including Piedmont. Results reported in a recentpaper (Brunetti et al., 2006) suggest that, when precipitation trends innorth-western Italy are considered, the significance of trend resultsis very sensitive to the choice of the time window considered. InSwitzerland, an increase of the frequency of convective precipitationevents in the second half of the 20th century has also been reported

(Stefanicki et al., 1998). Extension of our analysis also to otherstatistics, which characterize the intra-annual temporal distribution ofobserved precipitation and the nature of precipitation events,indicates the absence of significant trends in all the variablesconsidered.

3.2. Temperature

Visual inspection of ensemble averages of annual means ofminimum and maximum temperature anomalies, shown in Fig. 6,reveals a significant and quite abrupt increase in temperatures start-ing in themid 80s. A linear fit to the ensemble-averaged annual meansof minimum and maximum temperature anomalies confirms sig-nificant and quite large trends of 0.023 °C/year for maximum tempe-ratures and of 0.012 °C/year for minimum temperatures. Fig. 7 showsthe ensemble-averaged time series of the mean maximum and mini-mum temperature anomalies in the four seasonal periods.

Fig. 8. Linear trends over the full period 1952–2002 of temperature data from eachstation as a function of station elevation. Only stations that presented at least 25 years ofvalid data (non-necessarily contiguous) were used. Significant (non-significant) trendsare marked with full (empty) circles. Upper panel: maximum temperatures; Lowerpanel: minimum temperatures. The significance of linear trends was assessed asdescribed in the text.

Fig.10.Daily averagemaximum (upper panel) andminimum (lower panel) temperatureanomalies with respect to the climatology in the three periods 1952–68 (dashed), 1969–85 (gray) and 1986–2002 (solid black). The gray bands indicate 95% confidence levelsassessed as explained in the text. Day counts start on December 1st.

190 N. Ciccarelli et al. / Global and Planetary Change 63 (2008) 185–195

Linear trend results, for the annual and the seasonal time series,are summarized in Table 1. The significance of the trends is assessed byusing the Monte-Carlo technique already described above. Summerand winter months show significant trends in maximum tempera-tures while minimum temperatures show a significant trend only insummer. Amarginally significant spring trend is identified by aMann–Kendall test but not by our Monte-Carlo technique. This is the onlycase where the Mann–Kendall and the Monte-Carlo tests givedifferent results. In all cases the residuals of the linear regressionpresent no heteroscedasticity and are found to be significantly uncor-related, following a standard Durbin–Watson test (Wilks, 1995).

Similarly to what was already done for precipitation, we explorewhether the linear trends are dominated by the influence of just a fewstations. To this end, Fig. 8 reports the linear trends of individualstation time series as a function of elevation. Only stations whichpresented at least 25 years of valid data are considered. The plotconfirms a positive trend for most stations and does not suggest adependence of trend magnitude on station elevation. Further, tempe-

Fig. 9. Mean maximum and minimum temperature anomalies (upper and lower panelsrespectively) in the three periods 1952–68 (triangles), 1969–85 (crosses) and 1986–2002 (circles), for each station, as a function of elevation. The station average over theentire period 1952–2002 has been subtracted from each value. The dashed lines show95% confidence bands obtained as explained in the text.

rature trends of individual stations show no dependence on stationlatitude (not shown).

To verify whether the increase in temperatures occurred mainly inrecent years (as suggested by the visual inspection of time seriesthemselves), we split the time series into three 17-year-long periods,namely: 1952–68, 1969–85 and 1986–2002. Average temperatureanomalies in these periods are reported in Fig. 9, again ordered bystation elevation. Also in this case, 95% confidence bands are obtainedby averaging temperature over a large set of randomly selected sets of17 years. We excluded stations which have more than 8 invalid yearsin each of the 17-year-long periods. As the plot shows, averagetemperature anomalies in the earlier two periods fall mostly withinthe confidence bands. In the most recent period, on the other hand,significantly higher temperatures are recorded. No obvious depen-dence on station elevation is detected.1

To further explore the seasonal behaviour of temperatureanomalies, in Fig. 10 we show the ensemble average of the maximumand minimum temperature anomalies in each day of the year, ave-raged over the NP=17 years of each period P, for the three periodsconsidered, i.e.

T̃ dð Þ ¼ 1

N

Xj

1

NP

XyaP

T 0j d; yð Þ �P

Tjdð Þ

h i

where d is the day, y is the year, Σj is the sum over the number ofactive stations, N, and Σy∈P is the sum over each period. To bettervisualize the fluctuations with respect to the climatology, wesubtracted from each curve the climatological curve obtained byaveraging over the whole period of study, T

Pj(d)= 1

Ny

Py T

0j d; yð Þ, where

Ny=51 is the total number of years from 1952 to 2002. Again, the graybands indicate 95% confidence bands obtained using the Monte-Carlotechnique described above. Consistent with previous results, max-imum temperatures show a significant increase inwinter and summerin the most recent period. Minimum temperatures show a significantincrease only in summer.

1 The fact that the temperature increase is concentrated in the last period, when thenumber of stations was already lower than in the previous two periods (see Fig. 2a),has been a reason of some concern. To verify whether such a temperature increasecould have been spuriously generated by the lower number of stations available in thelast twenty years, we repeated the analysis considering only those stations that wereactive in the period 1986–2002, and found the same results. This demonstrates thatthe temperature increase in the mid 80s is a regional signature and it is not due to thevariable number of active stations.

Table 2ERA40 temperature trends in °C/yearTable 2ERA40 temperature trends in °C/year

Significant trends are on a gray background. Significance is assessed as explained in the text.

191N. Ciccarelli et al. / Global and Planetary Change 63 (2008) 185–195

The analysis of the ERA40 data provides results that are consistentwith the analysis of the station dataset. As shown in Table 2, ERA40maximum temperatures over north-western Italy show a significantlinear increase, of about the same magnitude, when considered at theannual scale or for winter and summer months. Minimum tempera-tures also show a similar overall trend for annual means, but, diffe-rently from the station data, they do not show a significant trend insummer months.

To put the above results in the context of larger-scale climatevariability, in Fig. 11a we show a spatial map of ERA40 temperatureanomalies in the period 1986–2002 with respect to the average overthe entire period 1958–2002. The spatial distribution of the lineartrends that are significantly different from zero is shown in Fig. 11b.These plots show a pattern of significant positive trends over the Alps,in agreement with the observed increase in temperatures in north-western Italy reported above.

Fig. 11. Both panels refer to ERA40 maximum surface temperatures. a) Temperature anomalyin the period 1958–2002. Only coloured pixels show significant trends or changes in tempestation temperature time series using a 5% confidence level.

The positive temperature trends found in north-western Italy are ingoodagreementwith thesignificant increase inmaximumandminimumtemperatures in the Alps reported in Beniston (2005) and Böhm et al.(2001). In our study area, we find a smaller trend for minimumtemperatures, at variance with the decrease in the daily range whichhas been suggested for the entire Alpine area (Beniston et al., 1994).Temperatures in Piedmont and Valle d'Aosta appear to have increased,especially starting fromthemid1980s. Thisobservation is consistentwitha particularly intense warming in the 1980s which has been reported forthe entire Alpine region (Auer and Böhm, 1994; Beniston and Rebetez,1995) and which has been suggested to be connected to the influence ofthe NAO pattern (Beniston and Jungo, 2002). The lack of trends in totalprecipitation and the observed increase in temperatures have led to asignificant decrease of snow cover in the western Alps in the last twentyyears, with important consequences on the population dynamics of wildmountain ungulates (Jacobson et al., 2004).

4. Impact of large-scale circulation patterns

The leading teleconnectionpatternswhich interest the Atlantic andthewestern European area are theNorthAtlantic Oscillation (NAO), theEast Atlantic pattern (EA), the Scandinavian pattern (SCAN) and theEast Atlantic/West Russia (EAWR) pattern (Walker and Bliss, 1932;Wallace and Gutzler, 1981; Barnston and Livezey, 1987; Hurrell, 1995).

in the period 1986–2002 with respect to the 1958–2002 average values. b) Linear trendsrature. Trend significance was assessed with the Monte- Carlo technique described for

2 To test two time series against the null-hypothesis of no correlation, we create10,000 surrogate time series by randomly shuffling the first of the two series. Thelinear correlation of the original series is compared with the distribution ofcorrelations obtained between the randomly shuffled series and the second series.

Table 3Correlation of seasonal precipitation and temperature time series with main large-scalecirculation indices

Correlations significant at the 1% (5%) level are on a dark (light) gray background.

Table 3Correlation of seasonal precipitation and temperature time series with main large-scalecirculation indices

192 N. Ciccarelli et al. / Global and Planetary Change 63 (2008) 185–195

Positive phases of NAO lead to a significant reduction of the totalatmospheric moisture transport over southern Europe and theMediterranean (Hurrell, 1995) and to winters which are milder anddrier owing to reduced moisture flux convergence (Beniston and Jungo,2002). In the last two decades of the past century, NAO shifted from aprevalence of thenegativephase to a frequent appearanceof thepositivephase. Positive NAO phases have been found to reduce precipitation inspring in Italy (Wibig, 1999) and at high altitudes in the Alps (Benistonand Jungo, 2002; Beniston, 2005). Weak correlations of Alpineprecipitation with NAO in the past century have been reported inSchmidli et al. (2002) and, interestingly on amuch longer time scale, in astudy on precipitation reconstructions in the Alps back to 1500 AD(Casty et al., 2005).

The East Atlantic pattern (Barnston and Livezey, 1987) is in generalassociated, in its positive phase, with increased temperatures in Europeall over the year (Sáenz et al., 2001). On the other hand, it has beenreported not to be significantly correlated with precipitation in Italy(Wibig, 1999). Starting frommid 70s, this index has been dominated bythe positive phase.

A pattern similar to the EAWR pattern and to the EU-2 pattern byBarnston and Livezey (1987) has been reported in Wibig (1999) toaffect positively precipitation in south-western Europe.

The presence of an anticyclone over northern Scandinavia (positiveSCAN phases) has been found to lead to increased precipitation overItaly, particularly in spring (Wibig,1999). The appearance of this patternhas been suggested to be associated with extreme precipitation inwestern Europe in autumn, such as the October 2000 episode (Black-burn and Hoskins, 2001). The last two decades have seen a predomi-nance of the negative phase of this index with respect to earlier years.

Positive phases of the SCAN pattern are closely related to thepresence of a blocking anticyclone over Scandinavia. A measure ofblocking frequency in the northern hemisphere was discussed inTibaldi andMolteni (1990) and in Tibaldi et al. (1994), where the Euro-Atlantic (EAB) and the European (EB) blocking indices were intro-duced. The positive phases of EAB and EBwere found byQuadrelli et al.(2001) to be correlated respectively with an overall increase in preci-pitation over the Alps and with a dipole leading to increased preci-pitation on the southern side of the Alps. At the same time, blocking(non-blocking) episodes were found to correspond to temperatureincreases (decreases) on the south flank of the Alps (Trigo et al., 2004).In the same work, a meridional shift of the location of maximumactivity of transient eddies as a consequence of blockingwas suggestedto be the cause of such behaviour.

We computed the correlation of the seasonally-averaged pre-cipitation and temperature time series with the NAO, SCAN, EA andEAWR teleconnection indices, using time series publicly availablefrom the NOAA Climate Prediction Centre (http://www.cpc.noaa.gov) and with the EAB and EB indices (using data courtesy of ARPA-SIM, the Regional Hydro-Meteorological Service of Emilia-Romagna,

Italy Quadrelli et al., 2001). The teleconnection patterns were allcomputed from NCEP reanalysis data in the period 1950–2000. Thecorrelations are reported in Table 3. Correlations significant at the1% and 5% level are reported on dark and light gray backgroundsrespectively. Significance of correlations was determined using botha standard Student's t-test and a shuffling Monte-Carlo method2,obtaining the same results in all cases. Detrending the time seriesdoes not change significantly the results.

In winter, NAO displays a strong positive correlation with tem-perature and a negative correlation with precipitation. No significantcorrelation is found in other seasons.

EA is correlated positively with temperatures in all seasons (exceptmaxima in summer), particularly withminimum temperatures. We donot have a physical explanation onwhy the correlation of EA should bestronger with minimum rather than maximum temperatures, and inany event the difference in the correlations is not large. The EA index isnot significantly correlated with precipitation.

SCAN appears as a very important index in all seasons: Its positivephase anticorrelates strongly with maximum temperatures fromspring to fall and it correlates positively with precipitation in allseasons, particularly in fall. It anticorrelates with minimum tem-peratures only in summer.

The EAWR index displays no significant correlation with observedtemperatures and precipitation.

To summarize these results, we find that, while temperatures arepositively correlated with the EA pattern all over the year, NAO plays arole, with a positive correlation, only in winter. In other seasons it isthe SCAN index which affects (negatively) temperatures in the studyarea. Precipitation is (negatively) correlated with NAO only in winter,while a positive phase of SCAN leads to increased precipitation all overthe year. In general these results are consistent with the role of SCANfor precipitation in Italy discussed in Wibig (1999).

The analysis of the blocking frequency indices indicates that theEuro-Atlantic blocking index has correlations that are similar to thoseof the negative phase of NAO: Low temperatures in winter and springare associated with the positive phase of EAB. The index shows nosignificant correlation with precipitation, in contrast with the resultsreported in Quadrelli et al. (2001). The European blocking (EB) indexshows, not surprisingly, a behaviour analogous to the SCAN index: It isassociated with high precipitation in summer and fall and with lowtemperatures from winter to summer. The correlation with tempera-ture is consistent with the impact of blocking episodes on wintertemperatures in Europe discussed in Trigo et al. (2004).

While most of these results agree with and confirmwhat is alreadyknown on the influence of large-scale circulation patterns on Alpineclimate, they also highlight a particular influence of the ScandinavianPattern and of northern European blocking episodes on the climate innorth-western Italy, with a positive correlation with precipitation anda negative correlation with temperatures.

5. Interannual variability

The correlation with large-scale circulation patterns discussedabove and the fact that precipitation in Alpine regions has been shownto display decadal variability (Schmidli et al., 2002), lead us to explorethe interannual variability in yearly and seasonally-averaged time se-ries by means of spectral analysis. Power spectra of temperatureanomalies and of standardized precipitation data consistently indicatethe absence of significant spectral peaks. Fig. 12 shows, as an example,the power spectra of the yearly-averaged time series. The spectra arecompared with the null hypothesis of autocorrelated noise by

Fig. 12. Power spectra of yearly-averaged standardized precipitation (a) and ofminimum (b) and maximum (c) temperature anomalies. The upper and lower dashedlines indicate respectively the 99% and 95% confidence levels obtained as explained inthe text.

Fig. 13. The two leading annual precipitation EOFs, explaining respectively 56% and 10%of precipitation variance.

193N. Ciccarelli et al. / Global and Planetary Change 63 (2008) 185–195

comparison with an autoregressive model. After estimating theparameters for an AR(1) stochastic processwe generated 1000 randomrealizations, fromwhichwe derived the 95% and 99% confidence bands

Table 4Variance explained by the two leading precipitation EOFs

Year (%) Winter (%) Spring (%) Summer (%) Fall (%)

PC1 56.4 73.5 66.6 48.7 71.5PC2 9.8 8.8 9.7 11.9 6.7

reported in the figures. All spectra have a white noise appearance,suggesting a lack of correlations over interannual time scales. Thespectra showno significant peak above the 99% confidence level. Peakscorresponding to fluctuations at time scales of 7 and 21 years can befound forminimumtemperature and for precipitation respectively, butthey barely exceed the 95% confidence levels. An analysis of the sea-sonal time series provides similar results.

The analysis of spectral coherence of precipitation and tempera-ture with the main teleconnection indices that are correlated with thestation data, namely NAO and SCAN, provides negative results: Fol-lowing von Storch and Zwiers (1999) we verified that at all frequenciesthe coherence is never significantly different from zero.

6. Principal component analysis

The data density of the rain gauge network analysed hereallows for exploring the joint spatial/temporal variability of preci-pitation in the study area, by using Principal Component (PC)analysis. We use this technique to identify the main spatial pat-terns of climatic variability both at annual and seasonal scale. Wedo not consider temperatures, owing to the limited density of theobservational network.

Since the time series contain several data gaps, the time series ofprincipal components cannot be obtained by direct projection of the

Table 5Correlation of the second principal component of precipitation data with large-scalecirculation indices

Table 5Correlation of the second principal component of precipitation data with large-scalecirculation indices

Fig. 14. Time series of the first two Principal Components of yearly-averagedprecipitation data.

194 N. Ciccarelli et al. / Global and Planetary Change 63 (2008) 185–195

data onto Empirical Orthogonal Functions (EOFs) but rather by a least-squares estimate (von Storch and Zwiers, 1999). We tested the sig-nificance of the leading EOF patterns using the Monte-Carlo methoddiscussed in Overland and Preisendorfer (1982).3 This test reveals thatonly the first two precipitation EOF patterns, which together explainmore than 60% of the total variance, are significant; Table 4 lists theexplained variances for yearly and seasonal time series. The correspond-ing EOF patterns are displayed in Fig. 13 for the case of annually-averageddata. The seasonal patterns are very similar and are not shown.

The first pattern is an almost homogeneous precipitation distribu-tion, and it corresponds to a synchronous interannual variability ofprecipitation at all locations. A similar homogeneous response ofprecipitation in the entire Alpine area was obtained by Quadrelli et al.(2001). In that work, non-standardized precipitation data were used.For this reason, the resulting leading EOF pattern was similar to thelong-time average precipitation climatology and it reflected oro-graphic variations in average precipitation.

The second EOF describes a North–South dipole with a precipita-tion anomaly over the northern, high elevation, alpine areas and ananomaly with opposite sign over the southern part of the study area.In the following, we define as the positive phase the situation depictedin Fig. 13, presenting lower precipitation over the northern area andincreased precipitation over the southern area.

The two corresponding principal component time series are re-ported in Fig. 14 for the case of annually-averaged data. While the firsttime series, PC1, closely follows the behaviour of the average preci-pitation time series shown in Fig. 3, due to the uniformity of EOF1, thesecond time series, PC2, shows an interesting shift towards a predo-minance of the negative phase after 1980, associated with a significantnegative trend. Inspection of the seasonal results shows that this trendis associated mainly with the spring season. Given the form of EOF2,such a change corresponds to an increase of precipitation in thenorthern alpine area and a decrease in the southern area. In fact,conditional ensemble averages of standardized precipitation based on

3 After generating 1000 random independent time series for each station, presentinggaps at the same dates as the original data and obtained by resampling the originaltime series, we computed the variance explained by their EOF patterns. Choosing the95% percentile allows to test for the significance of the variance explained by the EOFsfound from analysis of the data.

station latitude present significant trends which confirm this result(see also Fig. 4).

The correlation of the time series of the second principal com-ponent of precipitation data with the main large-scale circulationindices is reported in Table 5. Due to the similarity of the time series,the first PC gives results identical to those already seen for preci-pitation ensemble-averaged over all stations. The second PC presents asignificant positive correlation with NAO in summer and with SCANand EB in winter and spring. It also shows a significant negativecorrelation with EA in spring and summer. A similar dipole shift ofprecipitation towards the southern flank of the Alps, associated withfrequent European blocking events in winter, is reported in Quadrelliet al. (2001) and in Trigo et al. (2004). The prevalence of a negativephase of SCAN in recent decades and the correlation found betweenPC2 and SCAN in winter and spring suggests that the observed long-term trend of the second principal component is linked with changesin this large-scale circulation pattern.

7. Discussion and conclusions

The careful statistical analysis of daily data from a dense networkof thermometric and rain gauge stations in north-western Italy from1952 to 2002 has allowed for quantitatively characterising climatevariability in the study area and for assessing similarities and dif-ferences with respect to climate fluctuations in other areas of theEuropean Alps.

One of the main results of this study is the confirmation of asignificant warming trend in average temperatures in north-westernItaly, of about 1 °C in the past 50 years, concentrated in winter andsummermonths. This trend agrees with the temperature trends foundin the independent ERA40 reanalysis temperature dataset over thesame area and with the general trend of increasing temperatures inthe Alps (Böhm et al., 2001; Beniston, 2005). A decrease of the dailytemperature range was not found in our study area (not shown). Theabsence of a significant trend in precipitation statistics confirms ageneral absence of precipitation trends in the Alps (Watson et al.,1997; Beniston, 2005) and indicates no detectable changes in theintra-annual distribution of precipitation. The presence of a significantwarming trend in summer months and the absence of any precipita-tion trend implies increased aridity, associated with larger evapo-transpiration due to the higher temperatures. Analogously, the winterwarming trend is associated with a reduced snow cover in the lasttwenty years over the Alps, with significant impact on mountainecosystems (Jacobson et al., 2004).

Significant correlations of large-scale circulation indices withregional climate variability have also been identified. The NorthAtlantic Oscillation (NAO) index is found to play a significant role onlyfor winter climate, with positive phases of NAO leading to warmwinters with reduced precipitation, in agreement with the resultsfrom other studies that considered larger European and Alpine regions(Wibig, 1999; Quadrelli et al., 2001; Beniston and Jungo, 2002;Beniston, 2005). The East Atlantic (EA) pattern is found to be signi-ficantly correlated with temperatures all over the year. The data alsoindicate a significant correlation of the Scandinavian (SCAN) pattern

Correlations significant at the 1% (5%) level are on a dark (light) gray background. Detrending theseries does not change significantively the results.

195N. Ciccarelli et al. / Global and Planetary Change 63 (2008) 185–195

with climate in the study area, with positive phases of SCAN leading toincreased precipitation, particularly in fall, and to a decrease in maxi-mum temperatures. Correspondingly, frequent blocking episodes overnorthern Europe are found to increase fall precipitation and to lead tocolder temperatures, an observation already made in Wibig (1999)and in Quadrelli et al. (2001) for the entire Alpine area.

Principal Component Analysis shows that most of the variability ofprecipitation in the study area is associated with a synchronous res-ponse at all locations. This pattern explains more that 50% of totalprecipitation variance. A second spatial pattern, corresponding to a shiftof precipitation between the northern and the southern area of north-western Italy, is found to be significant and it explains about 10% of thetotal precipitation variance. The same pattern is found to be correlatedwith the Scandinavian pattern and with frequent blocking episodes.

We believe that the characterization of climate variability in north-western Italy reported here is a necessary step to understand andempirically model climate change impacts on the hydrological budgetand on ecosystem functioning in this area. The study of some of theseimpacts has been reported elsewhere (Jacobson et al., 2004) and ageneral study of the response of glaciers in thewestern Alps to climatechange has been recently completed (Calmanti et al., 2007).

Acknowledgements

This work was supported by the Interreg IIIb-MEDOCC projectsSEDEMED I and II.

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