INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. (2013) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3671 Snow variability in Romania in connection to large-scale atmospheric circulation Marius-Victor Birsan * and Alexandru Dumitrescu Meteo Romania (National Meteorological Administration), Bucharest, Romania ABSTRACT: Daily measurements of snow depth, cumulated precipitation and mean temperature from 105 meteorological stations with continuous record over the 1961 – 2010 period were analysed for trends with the Mann – Kendall nonparametric test. Trends in the number of days with snow cover, and in the mean snow depth are decreasing at 29 and 18% of the stations, respectively. The decrease in snow depth affects the intra-Carpathian region and Northeastern Romania. The most dramatic change concerns the number of snowfall days, which is decreasing at 82% of the locations. There is a slight decrease in precipitation amount, significant at only 8% of the stations. The mean temperature is increasing at 47% of the stations, while the number of days with temperature over 0 ◦ C shows upward trends at 63% of the stations. Overall, the winter season in Romania has changed substantially. All snow-related parameters show significant negative correlations with the North Atlantic Oscillation (NAO) index for winter. The NAO has a strong impact throughout the country suggesting that the winter variability in Romania is driven by the large-scale circulation over the North Atlantic. Copyright 2013 Royal Meteorological Society KEY WORDS snow depth; snowfall; snow pack; temperature; Romania; North Atlantic Oscillation; Mann–Kendall trend test; teleconnections Received 5 January 2012; Revised 3 November 2012; Accepted 11 January 2013 1. Introduction Analysing changes in the extent and amount of snow is essential for the assessment of the impacts of climate variability of a region (R¨ ais¨ anen, 2008). Snow cover has major effects on surface albedo and energy balance, and represents a major storage of water. The snow pack strongly influences the overlying air, the underlying ground and the atmosphere downstream. (Vavrus, 2007). Snow cover duration influences the growing season of the vegetation at high altitudes (Keller et al ., 2005). A shortening snow season enhances soil warming due to increased solar absorption (Lawrence and Slater, 2010). Comprehensive studies on snow variability and their spatial patterns have been conducted at hemispheric scale by Brown (2000) and Dye (2002), who demonstrated the snow cover decrease in response to recent warming. In Europe, studies on snow variability have been done for Switzerland (Laternser and Schneebeli, 2003; Scherrer and Appenzeller, 2006; Marty and Blanchet, 2012), France (Durand et al ., 2009) and Italy (Valt and Cianfarra, 2010). In Finland, trends towards shorter snow season and greater snow depth have been noted by Hyv¨ arinen (2003). Ye and Ellison (2003) had similar conclusions for the former Soviet Union for 1936–1995. The North Atlantic Oscillation (NAO) has been shown to influence the winter precipitation and snow cover west * Correspondence to: M.-V. Birsan, Meteo Romania (National Meteorological Administration), Sos. Bucuresti-Ploiesti 97, 013686 Bucharest, Romania. E-mail: [email protected]of −30 ◦ E (Bednorz, 2004). Beniston (1997) found the NAO influencing the timing and amount of snow in the Swiss Alps. Bednorz (2002) showed that the NAO exhibited strong negative correlations with the snow cover duration over western Poland. For Bulgaria, Brown and Petkova (2007) associated the years having high snow accumulation with a negative NAO pattern. Previous hydroclimatic studies in Romania have mainly focused on precipitation (Busuioc and von Storch, 1996; Tomozeiu et al ., 2005), temperature (Tomozeiu et al ., 2002) and streamflow (Rimbu et al ., 2002, 2004; Birsan et al ., 2012). The snow variability in Romania was examined by Cazacioc and Cazacioc (2005) for 1961–1990, concluding that the NAO- positive phase leads to less snowy winter months in Romania, whereas the NAO-negative phase increases the probability of snowy winters. Bojariu and Dinu (2007) using monthly data for 1961–2000 had similar findings, suggesting that the diminishing snow depth over the country was related to the tendency toward the positive phase of NAO (Popova, 2007). Here we present an analysis of winter trends in Romania for the 1961–2010 period using daily data; connections between the NAO and the Romanian snow pack are also investigated. 2. Data Romania is the largest country in Eastern Europe, with an area of 238 391 km 2 . The terrain is fairly equally distributed between mountainous, hilly and lowland Copyright 2013 Royal Meteorological Society
Transcript
INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. (2013)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.3671
Snow variability in Romania in connection to large-scaleatmospheric circulation
Marius-Victor Birsan* and Alexandru DumitrescuMeteo Romania (National Meteorological Administration), Bucharest, Romania
ABSTRACT: Daily measurements of snow depth, cumulated precipitation and mean temperature from 105 meteorologicalstations with continuous record over the 1961–2010 period were analysed for trends with the Mann–Kendall nonparametrictest. Trends in the number of days with snow cover, and in the mean snow depth are decreasing at 29 and 18% of thestations, respectively. The decrease in snow depth affects the intra-Carpathian region and Northeastern Romania. Themost dramatic change concerns the number of snowfall days, which is decreasing at 82% of the locations. There is aslight decrease in precipitation amount, significant at only 8% of the stations. The mean temperature is increasing at47% of the stations, while the number of days with temperature over 0 ◦C shows upward trends at 63% of the stations.Overall, the winter season in Romania has changed substantially. All snow-related parameters show significant negativecorrelations with the North Atlantic Oscillation (NAO) index for winter. The NAO has a strong impact throughout thecountry suggesting that the winter variability in Romania is driven by the large-scale circulation over the North Atlantic.Copyright 2013 Royal Meteorological Society
KEY WORDS snow depth; snowfall; snow pack; temperature; Romania; North Atlantic Oscillation; Mann–Kendall trend test;teleconnections
Received 5 January 2012; Revised 3 November 2012; Accepted 11 January 2013
1. Introduction
Analysing changes in the extent and amount of snow isessential for the assessment of the impacts of climatevariability of a region (Raisanen, 2008). Snow coverhas major effects on surface albedo and energy balance,and represents a major storage of water. The snowpack strongly influences the overlying air, the underlyingground and the atmosphere downstream. (Vavrus, 2007).Snow cover duration influences the growing season ofthe vegetation at high altitudes (Keller et al ., 2005). Ashortening snow season enhances soil warming due toincreased solar absorption (Lawrence and Slater, 2010).
Comprehensive studies on snow variability and theirspatial patterns have been conducted at hemispheric scaleby Brown (2000) and Dye (2002), who demonstrated thesnow cover decrease in response to recent warming.
In Europe, studies on snow variability have beendone for Switzerland (Laternser and Schneebeli, 2003;Scherrer and Appenzeller, 2006; Marty and Blanchet,2012), France (Durand et al ., 2009) and Italy (Valtand Cianfarra, 2010). In Finland, trends towards shortersnow season and greater snow depth have been notedby Hyvarinen (2003). Ye and Ellison (2003) had similarconclusions for the former Soviet Union for 1936–1995.The North Atlantic Oscillation (NAO) has been shownto influence the winter precipitation and snow cover west
* Correspondence to: M.-V. Birsan, Meteo Romania (NationalMeteorological Administration), Sos. Bucuresti-Ploiesti 97, 013686Bucharest, Romania. E-mail: [email protected]
of −30◦E (Bednorz, 2004). Beniston (1997) found theNAO influencing the timing and amount of snow inthe Swiss Alps. Bednorz (2002) showed that the NAOexhibited strong negative correlations with the snowcover duration over western Poland. For Bulgaria, Brownand Petkova (2007) associated the years having highsnow accumulation with a negative NAO pattern.
Previous hydroclimatic studies in Romania havemainly focused on precipitation (Busuioc and vonStorch, 1996; Tomozeiu et al ., 2005), temperature(Tomozeiu et al ., 2002) and streamflow (Rimbu et al .,2002, 2004; Birsan et al ., 2012). The snow variabilityin Romania was examined by Cazacioc and Cazacioc(2005) for 1961–1990, concluding that the NAO-positive phase leads to less snowy winter months inRomania, whereas the NAO-negative phase increases theprobability of snowy winters. Bojariu and Dinu (2007)using monthly data for 1961–2000 had similar findings,suggesting that the diminishing snow depth over thecountry was related to the tendency toward the positivephase of NAO (Popova, 2007).
Here we present an analysis of winter trends inRomania for the 1961–2010 period using daily data;connections between the NAO and the Romanian snowpack are also investigated.
2. Data
Romania is the largest country in Eastern Europe, withan area of 238 391 km2. The terrain is fairly equallydistributed between mountainous, hilly and lowland
Copyright 2013 Royal Meteorological Society
M.-V. BIRSAN AND A. DUMITRESCU
Figure 1. The meteorological stations used in this study. This figure is available in colour online at wileyonlinelibrary.com/journal/joc
Figure 2. The cumulative frequency functions of the altitude of the meteorological network and the 1 km DEM distributions of the altitude ofthe stations and of the country DEM. This figure is available in colour online at wileyonlinelibrary.com/journal/joc
territories. It has a transitional climate between temperateand continental with four distinct seasons.
The data used in this study were extracted from theclimatic database of the Romanian National Meteorolog-ical Administration. The stations are located at elevationsranging from 1 to 2506 m.a.s.l., and have a good spatialcoverage across the country (Figure 1), as well as a fairaltitudinal distribution (Figure 2). All stations involvedin this study are listed in Table I. The analysis was con-ducted for the period 1961–2010, for the standard winterseason (DJF).
The time series consist in daily measurements from105 meteorological stations, i.e. all available data with
continuous records over the study period, and qualitycontrolled. The dataset contains no reconstructed records,like extensions or missing values filled by means ofcomputational algorithms. For all stations, all yearsthat were taken into account have full daily records.In the very rare cases when a station had missingvalue(s) during winter season, the respective year wasnot taken into account. The following parameters havebeen considered:
(1) daily snow depth (measured at 6 a.m. for the previousday);
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SNOW VARIABILITY IN ROMANIA IN CONNECTION WITH LARGE-SCALE CIRCULATION
Table I. List of the meteorological stations involved in the trend analysis, with their geographic coordinates, altitude and multi-annual mean snow depth, mean temperature and precipitation amount (DJF, 1961–2010).
(2) number of days with snow cover (the threshold for aday with snow cover is 1 cm);
(3) number of snowfall days (for the calendar day);(4) mean daily temperature (average of the four measure-
ments of the day, at 0, 6, 12 and 18 h);(5) number of days with minimum temperature above 0
◦C (for the calendar day);(6) mean daily precipitation (for the i -th day = the cumu-
lated precipitation between 18 h of the previous dayand 18 h of the day i ).
(7) maximum daily precipitation (for the i -th day = thecumulated precipitation between 18 h of the previousday and 18 h of the day i ).
We used the NAO index of Li and Wang (2003),defined as the difference in the normalized monthly sealevel pressure regionally zonal-averaged over the NorthAtlantic sector from 80◦W to 30◦E between 35◦N and65◦N.
We have also investigated the influence of the EastAtlantic/Western Russia (EAWR) index (Barnston andLivezey, 1987), a zonally oriented pattern that wasfound to influence the climatic variability in southeast-ern Europe and in Central and Eastern Mediterranean(Krichak and Alpert, 2005; Ziv et al ., 2006; Rimbu et al .,2012).
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SNOW VARIABILITY IN ROMANIA IN CONNECTION WITH LARGE-SCALE CIRCULATION
3. Methods
3.1. The Mann–Kendall trend test
The local significance of trends has been analysed withthe nonparametric Mann–Kendall (MK) test. The MKtest is a rank-based procedure, particularly suitable fornon-normally distributed data, data containing outliersand nonlinear trends (Salas, 1993). The null and thealternative hypothesis of the MK test for trend in therandom variable x are:{
H0 : Pr(xj > xi ) = 0.5, j > iHA : Pr(xj < xi ) �= 0.5, (two-sided test)
. (1)
The MK statistic S is calculated as
S =n−1∑k=1
n∑j=k+1
sgn(xj − xk
)(2)
where xj and xk are the data values in years j and k ,respectively, with j > k , n is the total number of yearsand sgn() is the sign function:
sgn(xj − xk
) =
1, if xj − xk > 00, if xj − xk = 0
−1, if xj − xk < 0. (3)
The distribution of S can be well approximated bya normal distribution for large n , with mean zero andstandard deviation given by:
σS =
√√√√√√n (n − 1) (2n + 5) −m∑
i=1
ti (i ) (i − 1) (2i + 5)
18.
(4)Equation (4) gives the standard deviation of S with the
correction for ties in data, with ti denoting the number ofties of extent i . The standard normal variable ZS is thenused for hypothesis testing.
ZS =
S−1σS
if S > 00 if S = 0
S+1σS
if S < 0. (5)
For a two-tailed test, the null hypothesis is rejected atsignificance level α if |Z |> Z α/2, where Z α/2 is the valueof the standard normal distribution with an exceedance
probability α/2. The significance level was fixed at 10%(two-tailed test).
3.2. Kendall–Theil slope estimate
The slope estimate b is conducted with the nonparamet-ric Kendall–Theil method (also known as Theil–Senslope estimate) which is suitable for a nearly linear trendin the variable x and is less affected by non-normaldata and outliers (Helsel and Hirsch, 1992). The slope iscomputed between all pairs i of the variable x :
βi = xj − xk
j − k, with j >k ; j = 2, . . . , n; k = 1, . . . , n − 1
(6)where i = 1 . . . N . For n values in the time series xthis will result in N = n (n –1)/2 values of β. The slopeestimate b is the median of β i, i = 1 . . . N .
3.3. Spearman rank correlation
Spearman’s rho is a nonparametric rank-based correla-tion coefficient used to estimate the monotone associationbetween two random variables. It is computed from thedifference d between the ranks of independently sortedvariables x and y (Kottegoda and Rosso 1997):
ρ = 1 −6
n∑i=1
d2i
n(n2 − 1
) . (7)
Under the null hypothesis of no correlation between xand y , the distribution of ρ can be approximated by anormal distribution with mean µρ and variance σ 2
ρ givenby {
µρ = 0σ 2
ρ = 1(n−1)
. (8)
The random variables x and y are considered correlatedat the significance level α if |ρ| > Zα/2/
√n − 1 for a
two-tailed test.
4. Results and discussion
The MK trend test applied to the daily climatic dataseries revealed substantial changes in the winter seasonin Romania; significant negative correlations between thesnow parameters and the NAO index have been found.
Table II. Summary of the trend analysis, linear slopes and correlation with the NAO index. Correlations are negative for snowand precipitation, and positive for temperature.
Stations showingtrends (% of total)
Linear slope of trendsa Stations presenting correlationswith NAO (% of total)
Downward Upward Median Lower quartile Upper quartile p < 0.05 p < 0.01
Mean snow depth 18 2 −0.03 −0.05 −0.01 74 45Days with snow cover 29 – −0.22 −0.33 −0.09 68 51Days with snowfall 82 – −2.36 −3.20 −1.86 95 70Precipitation 8 – 0.03 −0.01 0.00 90 72Mean temperature – 47 0.03 0.02 0.03 60 37Days with positive temperature – 64 0.03 0.11 0.23 18 11
aAll slopes are considered, regardless the trend significance.
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M.-V. BIRSAN AND A. DUMITRESCU
(a)
(b)
Figure 3. Trends in mean snow depth (a) and in the number of days with snow coverage (b); downward blue triangles signify decreasingtrends, upward red triangles denote increasing trends and white circles symbolize no trend. This figure is available in colour online at
wileyonlinelibrary.com/journal/joc
Figure 4. Trends in mean snow depth (MK Z score) versus altitude. The two bold lines show the thresholds for the 10% significance levels (i.e.the dots found in between are not significant).
They are summarized in Table II and discussed in thefollowing sub-sections.
4.1. Snow trends
Downward trends have been found in the number ofdays with snow coverage at 29% of the locations
(Figure 3(a)), and in the mean snow depth for 18%of the stations (Figure 3(b)). The greatest stability ofsnow cover is recorded in the mountains (Figure 4),similar to the findings for Poland (Falarz, 2004, 2007).While mountainous regions are sensible to climate change(Beniston, 2003; Lopez-Moreno et al ., 2011), the snow
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SNOW VARIABILITY IN ROMANIA IN CONNECTION WITH LARGE-SCALE CIRCULATION
Figure 5. Evolution of the mean snow depth (left) and number of snowfall days (right) at five locations. The stations are (from top tobottom): Deva (230 m), Dumbraveni (323 m), Fagaras (435 m), Fundata (1376 m) and Iezer (1792 m). This figure is available in colour online at
wileyonlinelibrary.com/journal/joc
cover and duration seems stable at high altitudes forthe standard winter DJF season. Micu (2009) reporteda lower incidence of snow cover at some mountainstations for the November to April interval, during1961–2003.
The trends have a spatial pattern, indicating that theintra-Carpathian region and Northeastern Romania arethose most affected by the snow pack alterations.
The most abrupt change concerns the number ofsnowfall days – which is decreasing at 82% of the
stations, suggesting an acceleration of the water cycle(Huntington, 2006): increased winter temperaturesmay result in increasing precipitation following theClausius–Clapeyron relationship, depending on theslope of the snowfall–temperature relationship (Daviset al ., 1999). Most of the locations with decreasingtrends in snowfall days show no decrease in the snowdepth, indicating that the same amount of snow tends tofall within a considerably shorter interval. Increases insnowfall intensity could be explained by an increased
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M.-V. BIRSAN AND A. DUMITRESCU
(a)
(b)
Figure 6. Trends in mean temperature (a) and in the number of days with positive temperature (Tmin > 0 ◦C) (b); upward red triangles denoteincreasing trends and white circles symbolize no trend. This figure is available in colour online at wileyonlinelibrary.com/journal/joc
moisture-holding capacity of the warmer atmosphere.Another explanation for the less altered mean snowdepth, despite a considerable decrease in the numberof days with snowfall, could be that mainly the lightsnowfall events are affected by transition to rain, whilethe heavy snowfall events (which are mainly responsiblefor the mean snow depth) remain unaffected by thetransition to rain.
The evolution of mean snow depth and of thenumber of snowfall days are continuous throughoutthe analysed period, as shown in Figure 5 for fivestations.
4.2. Precipitation and temperature trends
Winter precipitation is also decreasing, but at only 8% ofthe stations. Maximum daily precipitation shows mixedtrends at 12% of the locations (6% increasing and 6%decreasing). Overall, the winter precipitation amount isfairly stable.
The mean daily temperature presents increasing trendsat 47% of the stations (Figure 6(a)), and the number ofdays with temperature above 0 ◦C increases at 63% ofthe stations (Figure 6(b)).
The results are in agreement with the previous studieson climatic variables in Romania (Busuioc and vonStorch, 1996; Tomozeiu et al ., 2005). The increase inwinter temperature (Tomozeiu et al ., 2002), togetherwith the few decreasing trends in precipitation, justifythe negative trends in snowfall days, indicating ahigher amount of precipitation falling as rain instead ofsnow (Birsan et al ., 2005; McCabe and Wolock, 2010;Pellicciotti et al ., 2010; Serquet et al ., 2011).
4.3. Influence of large-scale atmospheric circulation onsnow amount and duration
There is a strong relationship between snow variabilityand the NAO – which affects the strength of westerlyflow and weather patterns in Europe in particular inwinter (Hurrell and van Loon, 1997; Rodwell et al .,1999; Wanner et al ., 2001; Bojariu and Gimeno, 2003).The advective processes exerted by the large-scalecirculation have the dominant influence on the spatialdistribution and temporal variation of European climateduring winter (Kuttel et al ., 2011). In Romania, thepositive thermal anomalies and the negative precipitationanomalies are associated with a high NAO index(Bojariu and Paliu, 2001).
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SNOW VARIABILITY IN ROMANIA IN CONNECTION WITH LARGE-SCALE CIRCULATION
(a)
(b)
Figure 7. Correlation of the NAO DJF index with: (a) the mean snow depth (DJF); (b) the number of days with snow coverage (DJF); redand orange dots symbolize significance levels of 99% (p < 0.01) and 95% (p < 0.05), respectively; all correlations are negative. This figure is
available in colour online at wileyonlinelibrary.com/journal/joc
Negative correlations between the DJF NAO index andthe number of days with snow coverage (Figure 7(a))have been found at half of the stations (at 0.01 p-level).The mean snow depth (Figure 7(b)) shows a similarcorrelation pattern. The NAO index correlates best withthe number of snowfall days (for 70% of the stations)and with the precipitation amount (72%).
While it cannot be claimed that the NAO completelyexplain the snow variability in Romania, the winterseason is clearly driven by the large-scale circulation overthe North Atlantic. The NAO signal is strong enough tooverpass the orography.
Strong negative correlations were found betweenEAWR and the winter precipitation amount, for 59%of the stations, at 0.01 p-level. However, neither snowamount and duration, nor temperature showed significantcorrelations with EAWR.
5. Conclusions
We presented a statistical analysis of trends in dailysnow depth, precipitation and temperature, using dailydata from 105 meteorological stations in Romania for 49winters (1961–2010). Significant trends were identified
for each station for the classic winter season (DJF).Connections between climatic time series and large-scale atmospheric circulation were investigated usingSpearman’s rank correlation coefficient and the NAOindex. The main conclusions are:
(1) Substantial changes in snow depth and durationhave occurred, with 29% of the stations presentingdecreasing trends in the number of days with snowcoverage; at 18% of the stations the mean snow depthalso shows decreasing trends. The snow pack at ele-vations above 500 m.a.s.l. is rather stable throughoutthe winter season. The intra-Carpathian region is themost affected by the decrease in snow cover.
(2) The number of snowfall days decreased at 82%of the stations, suggesting a major change of thewinter season; snowfalls are getting shorter and moreintense.
(3) The increase in the number of days with temperatureabove 0 ◦C at 63% of the locations, together withthe slight decrease in winter precipitation explainsthe diminution of the snowfall days.
(4) The number of snowfall days, snow duration andmean snow depth present strong negative correlations
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M.-V. BIRSAN AND A. DUMITRESCU
with the NAO index for the same period (DJF). Thelarge-scale circulation over the North Atlantic has aconsiderable effect on the Romanian winter season.
Acknowledgements
We thank the two anonymous reviewers for their mean-ingful remarks and suggestions that led to a significantlyimproved paper. This work has been done within theframework of the project CLIMHYDEX (Changes in Cli-mate Extremes and Associated Impact in HydrologicalEvents in Romania), funded by the Executive Agency forHigher Education, Research, Development and Innova-tion Funding (project ID: PNII-ID-PCCE-2011-2-0073).
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