Snow variability in Romania in connection to large-scale atmospheric circulation

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: (214) Published online 21 February 213 in Wiley Online Library (wileyonlinelibrary.com) DOI:.2/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 5 meteorological stations with continuous record over the 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 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. KEY WORDS snow depth; snowfall; snow pack; temperature; Romania; North Atlantic Oscillation; Mann Kendall trend test; teleconnections Received 5 January 212; Revised 3 November 212; Accepted 11 January Introduction Analysing changes in the extent and amount of snow is essential for the assessment of the impacts of climate variability of a region (Räisänen, 28). 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, 27). Snow cover duration influences the growing season of the vegetation at high altitudes (Keller et al., 25). A shortening snow season enhances soil warming due to increased solar absorption (Lawrence and Slater, 2). Comprehensive studies on snow variability and their spatial patterns have been conducted at hemispheric scale by Brown (2) and Dye (22), 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, 23; Scherrer and Appenzeller, 26; Marty and Blanchet, 212), France (Durand et al., 29) and Italy (Valt and Cianfarra, 2). In Finland, trends towards shorter snow season and greater snow depth have been noted by Hyvärinen (23). Ye and Ellison (23) had similar conclusions for the former Soviet Union for 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, Bucharest, Romania. marius.birsan@gmail.com of 3 E (Bednorz, 24). Beniston (1997) found the NAO influencing the timing and amount of snow in the Swiss Alps. Bednorz (22) showed that the NAO exhibited strong negative correlations with the snow cover duration over western Poland. For Bulgaria, Brown and Petkova (27) 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., 25), temperature (Tomozeiu et al., 22) and streamflow (Rimbu et al., 22, 24; Birsan et al., 212). The snow variability in Romania was examined by Cazacioc and Cazacioc (25) for , concluding that the NAOpositive phase leads to less snowy winter months in Romania, whereas the NAO-negative phase increases the probability of snowy winters. Bojariu and Dinu (27) using monthly data for had similar findings, suggesting that the diminishing snow depth over the country was related to the tendency toward the positive phase of NAO (Popova, 27). Here we present an analysis of winter trends in Romania for the period using daily data; connections between the NAO and the Romanian snow pack are also investigated. 2. Romania is the largest country in Eastern Europe, with an area of km 2. The terrain is fairly equally distributed between mountainous, hilly and lowland 213 Royal Meteorological Society

2 SNOW VARIABILITY IN ROMANIA IN CONNECTION WITH LARGE-SCALE CIRCULATION 135 Figure 1. The meteorological stations used in this study. Figure 2. The cumulative frequency functions of the altitude of the meteorological network and the 1 km DEM distributions of the altitude of the stations and of the country DEM. territories. It has a transitional climate between temperate and continental with four distinct seasons. The data used in this study were extracted from the climatic database of the Romanian National Meteorological Administration. The stations are located at elevations ranging from 1 to 256 m.a.s.l., and have a good spatial coverage across the country (Figure 1), as well as a fair altitudinal distribution (Figure 2). All stations involved in this study are listed in Table 1. The analysis was conducted for the period , for the standard winter season (DJF). The time series consist in daily measurements from 5 meteorological stations, i.e. all available data with continuous records over the study period, and quality controlled. The dataset contains no reconstructed records, like extensions or missing values filled by means of computational algorithms. For all stations, all years that were taken into account have full daily records. In the very rare cases when a station had missing value(s) during winter season, the respective year was not taken into account. The following parameters have been considered: (1) daily snow depth (measured at 6 a.m. for the previous day);

3 136 M.-V. BIRSAN AND A. DUMITRESCU Table 1. List of the meteorological stations involved in the trend analysis, with their geographic coordinates, altitude and multiannual mean snow depth, mean temperature and precipitation amount (DJF, ). Station ID Station name Longitude (decimal degrees) Latitude (decimal degrees) Altitude (m.a.s.l.) Mean snow depth (cm) Mean DJF temperature ( C) Mean DJF precipitation amount (mm) Mangalia Sfantu Gheorghe (Delta) Gorgova Tulcea Constanta Calarasi Giurgiu Turnu Magurele Jurilovca Bechet Harsova Grivita Tecuci Bailesti Calafat Urziceni Medgidia Fundulea Galati Drobeta Turnu Severin Bucuresti Filaret Banloc Alexandria Sannicolau Mare Timisoara Buzau Bucuresti Baneasa Adjud Iasi Caracal Rosiorii de Vede Arad Videle Vaslui Sacuieni Satu Mare Oradea Ramnicu Sarat Adamclisi Varadia de Mures Botosani Lugoj Barlad Ploiesti Bacau Craiova Targu Jiu Roman Corugea Baia Mare Stolnici Deva Dej Caransebes Ramnicu Valcea Targu Ocna Pades (Apa Neagra) Sebes (Alba) Targu Logresti Dragasani

4 SNOW VARIABILITY IN ROMANIA IN CONNECTION WITH LARGE-SCALE CIRCULATION 137 Station ID Station name Longitude (decimal degrees) Table 1. continued Latitude (decimal degrees) Altitude (m.a.s.l.) Mean snow depth (cm) Mean DJF temperature ( C) Mean DJF precipitation amount (mm) Stei Sighetul Marmatiei Targoviste Cotnari Patarlagele Zalau Oravita Targu Mures Dumbraveni Pitesti Blaj Piatra Neamt Suceava Bistrita Targu Neamt Radauti Cluj-Napoca Fagaras Curtea de Arges Sibiu Campina Baraolt Boita Polovragi Brasov Dedulesti-Moraresti Targu Secuiesc Petrosani Miercurea Ciuc Campulung Muscel Toplita Intorsura Buzaului Predeal Baisoara Fundata Semenic Cuntu Paltinis Sinaia Parang Lacauti Iezer Vladeasa Tarcu Varful Omu (2) number of days with snow cover (the threshold for a day with snow cover is 1 cm); (3) number of snowfall days (for the calendar day); (4) mean daily temperature (average of the four measurements of the day, at, 6, 12 and 18 h); (5) number of days with minimum temperature above C (for the calendar day); (6) mean daily precipitation (for the i-th day= the cumulated precipitation between 18 h of the previous day and 18 h of the day i). (7) maximum daily precipitation (for the i-th day = the cumulated precipitation between 18 h of the previous day and 18 h of the day i). We used the NAO index of Li and Wang (23), defined as the difference in the normalized monthly sea level pressure regionally zonal-averaged over the North Atlantic sector from 8 Wto3 E between 35 Nand 65 N. We have also investigated the influence of the East Atlantic/Western Russia (EAWR) index (Barnston and Livezey, 1987), a zonally oriented pattern that was found to influence the climatic variability in southeastern Europe and in Central and Eastern Mediterranean (Krichak and Alpert, 25; Ziv et al., 26; Rimbu et al., 212).

5 138 M.-V. BIRSAN AND A. DUMITRESCU 3. Methods 3.1. The Mann Kendall trend test The local significance of trends has been analysed with the nonparametric Mann Kendall (MK) test. The MK test is a rank-based procedure, particularly suitable for non-normally distributed data, data containing outliers and nonlinear trends (Salas, 1993). The null and the alternative hypothesis of the MK test for trend in the random variable x are: { H : Pr(x j > x i ) =.5, j > i. (1) H A : Pr(x j < x i ).5, (two-sided test) The MK statistic S is calculated as n 1 n S = sgn ( ) x j x k (2) k=1 j =k+1 where x j and x k are the data values in years j and k, respectively, with j > k, n is the total number of years and sgn() is the sign function: sgn ( ) 1, if x j x k > x j x k =, if x j x k =. (3) 1, if x j x k < The distribution of S can be well approximated by a normal distribution for large n, with mean zero and standard deviation given by: m n (n 1)(2n + 5) t i (i)(i 1)(2i + 5) i=1 σ S =. 18 (4) Equation (4) gives the standard deviation of S with the correction for ties in data, with t i denoting the number of ties of extent i. The standard normal variable Z S is then used for hypothesis testing. Z S = S 1 σ S if S > ifs = S +1 σ S if S <. (5) For a two-tailed test, the null hypothesis is rejected at significance level α if Z > Z α/2, where Z α/2 is the value of the standard normal distribution with an exceedance probability α/2. The significance level was fixed at % (two-tailed test) Kendall Theil slope estimate The slope estimate b is conducted with the nonparametric Kendall Theil method (also known as Theil Sen slope estimate) which is suitable for a nearly linear trend in the variable x and is less affected by non-normal data and outliers (Helsel and Hirsch, 1992). The slope is computed between all pairs i of the variable x: β i = x j x k, with j >k; j = 2,..., n; k = 1,..., n 1 j k (6) where i = 1...N. For n values in the time series x this will result in N = n (n 1)/2 values of β. The slope estimate b is the median of β i, i = 1...N Spearman rank correlation Spearman s rho is a nonparametric rank-based correlation coefficient used to estimate the monotone association between two random variables. It is computed from the difference d between the ranks of independently sorted variables x and y (Kottegoda and Rosso 1997): n 6 i=1 ρ = 1 n ( ). (7) n 2 1 Under the null hypothesis of no correlation between x and y, the distribution of ρ can be approximated by a normal distribution with mean µρ and variance σρ 2 given by { µρ = σρ 2 = 1. (8) (n 1) The random variables x and y are considered correlated at the significance level α if ρ > Z α/2 / n 1fora two-tailed test. 4. Results and discussion The MK trend test applied to the daily climatic data series revealed substantial changes in the winter season in Romania; significant negative correlations between the snow parameters and the NAO index have been found. d 2 i Table 2. Summary of the trend analysis, linear slopes and correlation with the NAO index. Correlations are negative for snow and precipitation, and positive for temperature. Stations showing trends (% of total) Linear slope of trends a Stations presenting correlations with NAO (% of total) Downward Upward Median Lower quartile Upper quartile p <.5 p <.1 Mean snow depth Days with snow cover Days with snowfall Precipitation Mean temperature Days with positive temperature a All slopes are considered, regardless the trend significance.

6 SNOW VARIABILITY IN ROMANIA IN CONNECTION WITH LARGE-SCALE CIRCULATION 139 (a) (b) Figure 3. Trends in mean snow depth (a) and in the number of days with snow coverage (b); downward blue triangles signify decreasing trends, upward red triangles denote increasing trends and white circles symbolize no trend. Figure 4. Trends in mean snow depth (MK Z score) versus altitude. The two bold lines show the thresholds for the % significance levels (i.e. the dots found in between are not significant). They are summarized in Table 2 and discussed in the following sub-sections Snow trends Downward trends have been found in the number of days 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 of snow cover is recorded in the mountains (Figure 4), similar to the findings for Poland (Falarz, 24, 27). While mountainous regions are sensible to climate change (Beniston, 23; López-Moreno et al., 211), the snow

7 14 M.-V. BIRSAN AND A. DUMITRESCU DEVA Mean snow depth (cm) DEVA Number of snowfall days DUMBRAVENI DUMBRAVENI FAGARAS 2 15 FAGARAS FUNDATA FUNDATA IEZER IEZER Figure 5. Evolution of the mean snow depth (left) and number of snowfall days (right) at five locations. The stations are (from top to bottom): Deva (23 m), Dumbraveni (323 m), Fagaras (435 m), Fundata (1376 m) and Iezer (1792 m). cover and duration seems stable at high altitudes for the standard winter DJF season. Micu (29) reported a lower incidence of snow cover at some mountain stations for the November to April interval, during The trends have a spatial pattern, indicating that the intra-carpathian region and Northeastern Romania are those most affected by the snow pack alterations. The most abrupt change concerns the number of snowfall days which is decreasing at 82% of the stations, suggesting an acceleration of the water cycle (Huntington, 26): increased winter temperatures may result in increasing precipitation following the Clausius Clapeyron relationship, depending on the slope of the snowfall temperature relationship (Davis et al., 1999). Most of the locations with decreasing trends in snowfall days show no decrease in the snow depth, indicating that the same amount of snow tends to fall within a considerably shorter interval. Increases in snowfall intensity could be explained by an increased

8 SNOW VARIABILITY IN ROMANIA IN CONNECTION WITH LARGE-SCALE CIRCULATION 141 (a) (b) Figure 6. Trends in mean temperature (a) and in the number of days with positive temperature (T min > C) (b); upward red triangles denote increasing trends and white circles symbolize no trend. moisture-holding capacity of the warmer atmosphere. Another explanation for the less altered mean snow depth, despite a considerable decrease in the number of days with snowfall, could be that mainly the light snowfall events are affected by transition to rain, while the heavy snowfall events (which are mainly responsible for the mean snow depth) remain unaffected by the transition to rain. The evolution of mean snow depth and of the number of snowfall days are continuous throughout the analysed period, as shown in Figure 5 for five stations Precipitation and temperature trends Winter precipitation is also decreasing, but at only 8% of the stations. Maximum daily precipitation shows mixed trends at 12% of the locations (6% increasing and 6% decreasing). Overall, the winter precipitation amount is fairly stable. The mean daily temperature presents increasing trends at 47% of the stations (Figure 6(a)), and the number of days with temperature above C increases at 63% of the stations (Figure 6(b)). The results are in agreement with the previous studies on climatic variables in Romania (Busuioc and von Storch, 1996; Tomozeiu et al., 25). The increase in winter temperature (Tomozeiu et al., 22), together with the few decreasing trends in precipitation, justify the negative trends in snowfall days, indicating a higher amount of precipitation falling as rain instead of snow (Birsan et al., 25; McCabe and Wolock, 2; Pellicciotti et al., 2; Serquet et al., 211) Influence of large-scale atmospheric circulation on snow amount and duration There is a strong relationship between snow variability and the NAO which affects the strength of westerly flow and weather patterns in Europe in particular in winter (Hurrell and van Loon, 1997; Rodwell et al., 1999; Wanner et al., 21; Bojariu and Gimeno, 23). The advective processes exerted by the large-scale circulation have the dominant influence on the spatial distribution and temporal variation of European climate during winter (Küttel et al., 211). In Romania, the positive thermal anomalies and the negative precipitation anomalies are associated with a high NAO index (Bojariu and Paliu, 21).

9 142 M.-V. BIRSAN AND A. DUMITRESCU (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); red and orange dots symbolize significance levels of 99% (p <.1) and 95% (p <.5), respectively; all correlations are negative. Negative correlations between the DJF NAO index and the number of days with snow coverage (Figure 7(a)) have been found at half of the stations (at.1 p-level). The mean snow depth (Figure 7(b)) shows a similar correlation pattern. The NAO index correlates best with the number of snowfall days (for 7% of the stations) and with the precipitation amount (72%). While it cannot be claimed that the NAO completely explain the snow variability in Romania, the winter season is clearly driven by the large-scale circulation over the North Atlantic. The NAO signal is strong enough to overpass the orography. Strong negative correlations were found between EAWR and the winter precipitation amount, for 59% of the stations, at.1 p-level. However, neither snow amount and duration, nor temperature showed significant correlations with EAWR. 5. Conclusions We presented a statistical analysis of trends in daily snow depth, precipitation and temperature, using daily data from 5 meteorological stations in Romania for 49 winters (1961 2). Significant trends were identified for each station for the classic winter season (DJF). Connections between climatic time series and largescale atmospheric circulation were investigated using Spearman s rank correlation coefficient and the NAO index. The main conclusions are: (1) Substantial changes in snow depth and duration have occurred, with 29% of the stations presenting decreasing trends in the number of days with snow coverage; at 18% of the stations the mean snow depth also shows decreasing trends. The snow pack at elevations above 5 m.a.s.l. is rather stable throughout the winter season. The intra-carpathian region is the most affected by the decrease in snow cover. (2) The number of snowfall days decreased at 82% of the stations, suggesting a major change of the winter season; snowfalls are getting shorter and more intense. (3) The increase in the number of days with temperature above C at 63% of the locations, together with the slight decrease in winter precipitation explains the diminution of the snowfall days. (4) The number of snowfall days, snow duration and mean snow depth present strong negative correlations

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